FINAL PROGRAM and BOOK of ABSTRACTS

FINAL PROGRAM and BOOK OF ABSTRACTS

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS’20)

Liuzhou, China November 20–22, 2020

Organized by Technical Committee on Data Driven Control, Learning and Optimization, Chinese Association of Automation Beijing Jiaotong University Qingdao University

Locally Organized by

Guangxi University of Science and Technology

Sponsored by IEEE Beijing Section IEEE Industrial Electronics Society IEEE CIS Beijing Chapter

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IEEE Catalog Number: CFP20HAG-USB ISBN: 978-1-7281-5921-8

CONTENTS

Organizing Committee………………………………………………………………………………...1

Welcome Message from General Chairs……………………………………………………………3

Message from Technical Program Chairs…………………………………………………………...5

Keynote Address………………………………………………………………………………………7

Distinguished Lecture ……………………………………………………………………………… 11

Technical Program and Book of Abstracts…………………………………………………………19

Program at a Glance…………………………………………………………………………………84

DDCLS’20 Organizing Committee

General Chairs: Zhongsheng Hou, Qingdao University, China Simin Li, Guangxi University of Science and Technology, China

General Co-Chairs: Chenghong Wang, Chinese Association of Automation, China Xiongxiong He, Zhejiang University of Technology, China Guangxing Tan, Guangxi University of Science and Technology, China

Organizing Committee Chairs: Jing Wang, Beijing University of Chemical Technology, China Xisheng Dai, Guangxi University of Science and Technology, China

Technical Program Committee Chairs: Mingxuan Sun, Zhejiang University of Technology, China Huaguang Zhang, Northeastern University, China

Regional Chairs: Xiao’e Ruan, Xi’an Jiaotong University, China Junmin Li, Xidian University, China Fei Liu, Jiangnan University, China Yong Fang, Shanghai Universtiy, China Zhiqiang Ge, Zhengjiang University, China Xiaodong Li, Sun Yat-sen University, China Xiangyang Li, South China University of Technology, China Li Wang, North China University of Technology, China Tianjiang Hu, Sun Yat-sen University, China Aihua Zhang, Bohai University, China Yanjun Liu, Liaoning University of Technology, China Deqing Huang, Southwest Jiaotong University, China Haisheng Yu, Qingdao University, China Ying Zheng, Huazhong University of Technology, China Ruizhuo Song, University of Science & Technology Beijing, China Wenchao Xue, Academy of Mathematics and Systems Science, China Academy of Sciences, China Chuansheng Wang, Qingdao University of Science & Technology, China

Committee Members: Members of Technical Committee on Data Driven Control, Learning and Optimization and Invited Experts

Invited Session Chairs: Zengqiang Chen, Nankai University, China Darong Huang, Chongqing Jiaotong University, China Jing Na, Kunming University of Science and Technology, China Fei Qiao, Tongji University, China Senping Tian, South China University of Technology, China Qinglai Wei, Institute of Automation, Chinese Academy of Sciences, China Zhanshan Wang, Northeastern University, China Jinpeng Yu, Qingdao University, China Weiwei Che, Qingdao University, China Yi Liu, Zhejiang University of Technology, China Jiayan Wen, Guangxi University of Science and Technology, China

Subject Session Chairs: Zhihuan Song, Zhejiang University, China Dongbin Zhao, Institute of Automation, Chinese Academy of Sciences, China Xin Xu, National University of Defense Technology, China

Panel Discussion Chairs: Hongye Su, Zhejiang University, China Qunxiong Zhu, Beijing University of Chemical Technology, China

Zengguang Hou, Institute of Automation, Chinese Academy of Sciences, China Changhua Hu, Rocket Force University of Engineering, China Zhijian Ji, Qingdao University, China

Poster Session Chairs Xuhui Bu, Henan Polytechnic University, China Wei Ai, South China University of Technology, China Hongtao Ye , Guangxi University of Science and Technology, China

International Affairs Chairs: Danwei Wang, Nanyang Technological University, Singapore Chiang-Ju Chien, Huafan University, Taiwan, China Zhi-Qiang Gao, Cleveland State University, USA Youqing Wang, Shandong University of Science and Technology, China Shen Yin, Harbin Institute of Technology, China Bin Chu, University of Southampton, UK

Finance Chairs: Shangtai Jin, Beijing Jiaotong University, China Rongmin Cao, Beijing Information Science and Technology University, China

Publication Chairs: Mengqi Zhou, IEEE Beijing Section, China Dong Shen, Renmin University of China, China

Editorial Chairs: Ronghu Chi, Qingdao University of Science & Technology, China Yongchun Fang, Nankai University, China Shan Liu, Zhejiang University, China Deyuan Meng, Beihang University, China

Publicity Chairs: Weisheng Chen, Xidian University, China Long Cheng, Institute of Automation, Chinese Academy of Sciences, China Shuguo Yang, Qingdao University of Science & Technology, China Liang Cai, Guangxi University of Science and Technology, China

Secretaries: Chenkun Yin, Beijing Jiaotong University, China Shoufeng Zhang, Guangxi University of Science and Technology, China Yuwei Zhang, Guangxi University of Science and Technology, China Xiangsuo Fan, Guangxi University of Science and Technology, China

DDCLS’20 Welcome Message from General Chairs

Zhongsheng Hou Simin Li General Chair of DDCLS’20 General General Chair Chair of 2016of DDCLS’ DDCLS20 General Chair of 2016 DDCLS

Dear Friends and Colleagues,

On behalf of the Organizing Committee, it is our greatest pleasure to welcome you to the 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS’20), which is organized by Technical Committee on Data Driven Control, Learning and Optimization (DDCLO), Chinese Association of Automation, Beijing Jiaotong University and Qingdao University, locally organized by Guangxi University of Science and Technology, all are from China, and sponsored by IEEE Beijing Section, IEEE Industrial Electronics Society, IEEE CIS Beijing Chapter. The conference is held at Liuzhou Liudong Ramada Plaza Hotel, Liuzhou, Guangxi Province, China, November 20–22, 2020.

Data driven control and learning systems, together with model-based control methods forming the complete control theory, is an emerging hot research area in the field of automation engineering and in systems & control community. It focuses on control, learning and optimization for the plants whose models are unavailable. Although the study on data driven control and learning is still in the embryonic stage, it has attracted a great amount of attention within the systems and control community, such as the special issues published in the top journals: ACTA AUTOMATICA SINICA (2009), IEEE Transactions on Neural Networks (2011), Information Sciences (2013), IEEE Transactions on Industrial Informatics (2013), IEEE Transactions on Industrial Electronics (2015, 2017), and IET Control Theory & Applications (2015, 2016). The keyword ‘Data Driven Control’ was formally listed with the application code F030110 as a new research domain in the project catalog of the National Natural Science Foundation of China in 2019. Further, the data driven control and learning systems would be fundamental challenges in the coming age of the Internet of Things, Cyber-Physical Systems, Industry 4.0, China Manufacturing 2025, and Artificial Intelligence 2.0 under the big data environment, which is already on our road ahead but beyond the traditional systems & control methods.

3 As an inheritance of previous seven workshops, DDCLS’20 continues to attract broad interest throughout the world, with the submission of 317 papers. This reflects the increasing interest in our field, and meanwhile creates a difficult workload in evaluating the papers and organizing a cohesive program. We are fortunate to have an exceptional Technical Program Committee (TPC) that sorted through the evaluations and integrated the individual submissions into the final technical program described in the proceedings. We also want to thank our Organizing Committee for their invaluable assistance in arranging the diverse offerings at the conference, from registration and local arrangements to technical programs. Last but not least, we would like to express our deep appreciation to Guangxi University of Science and Technology for their great support.

The Technical Program Committee has assembled a comprehensive technical program that covers a broad spectrum of topics in data driven control and learning systems. The DDCLS’20 technical program comprises 14 regular sessions, 16 invited sessions, 1 best paper award session and 2 interactive sessions. Besides the technical sessions, the highlights of the DDCLS’20 are the keynote addresses given by distinguished senior scholars including Prof. Frank Allgöwer from Germany, Prof. Alessandro Astolfi from UK, Prof. Ben M. Chen from Hong Kong, China and Prof. Derong Liu from China, and the distinguished lectures given by active young scholars including Prof. Chunhui Zhao, Prof. Xiaoli Luan, Prof. Jun Zhao, Prof. Xiaosheng Si, Prof. Yuanjing Feng, Prof. Dong Shen, Prof. Keyou You and Prof. Qiuye Sun, all from China. We sincerely appreciate all the contributors, keynote address speakers, distinguished lecture speakers, invited session organizers, and session chairs for their tremendous efforts towards a top-quality conference.

We also want to thank the young lovely volunteers who have made this conference possible. Without you, the monumental task ahead of us for organizing this conference would be significantly beyond our capabilities.

May you have a wonderful and fascinating stay in Liuzhou, Guangxi Province, China, and enjoy the colorful scenery and magic foods.

Best wishes

Zhongsheng Hou Simin Li General Chair of DDCLS’20 General Chair of DDCLS’20

DDCLS’20 Message from Technical Program Chairs

Mingxuan Sun Huaguang Zhang Technical Program Chair Technical Program Chair

Dear Friends and Colleagues,

On behalf of the Technical Program Committee, it is our great honor to welcome you to the 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS’20) in Liuzhou, China.

The annual event of DDCLS has proven to be one of the excellent forums for scientists, researchers, engineers, and industrial practitioners to present and discuss the latest technological advancements as well as future directions and trends in Data Driven Control, Learning and Optimization, and to set up useful links for their works. DDCLS’20 has received enthusiastic responses with a total of 317 submissions. All the submissions had been processed by the Technical Program Committee. All committee members worked professionally, responsibly, and diligently. Besides evaluations from reviewers, each member also provided his/her own assessments on the assigned papers, so as to ensure that only high-quality papers would be accepted. Their commitment and hard work have enabled us to put together a very solid proceeding for our conference. The proceeding includes 261 papers which are divided into 31 oral sessions and 2 poster sessions for presentation.

Ahead of the parallel technical sessions, we will have four keynote talks to be delivered by eminent scientists. These lectures will address the state-of-the-art developments and leading-edge research topics in both theory and applications in Data Driven Control, Learning and Optimization. We are indeed honored to have Prof. Frank Allgöwer (University of Stuttgart), Prof. Alessandro Astolfi (Imperial College London), Prof. Ben M. Chen (Chinese University of Hong Kong), and Prof. Derong Liu (Guangdong University of Technology) as the keynote address speakers. Besides, we are very lucky to have the following distinguished lectures given by eight outstanding young scholars, they are Prof. Chunhui Zhao (Zhejiang University), Prof. Xiaoli Luan (Jiangnan University), Prof. Jun Zhao (Dalian University of Technology), Prof. Xiaosheng Si (Rocket Force University of Engineering), Prof. Yuanjing Feng (Zhejiang University of Technology), Prof. Dong Shen (Renmin University of China), Prof. Keyou You (Tsinghua University) and

5 Prof. Qiuye Sun (Northeastern University). We are confident that their presences would undoubtedly act prestige to the conference. We would like to express our sincere appreciations to all of them for their enthusiastic contributions and strong supports to DDCLS’20.

To promote the development of the society of Data Driven Control, Learning and Optimization, the highest quality papers will be rewarded with the Best Paper Award at DDCLS’20. Based on reviewers' comments and nominations as well as the evaluations of Technical Program Committee members, 24 papers were selected for the consideration of the award by the Best Paper Award Committee. These papers were sent to some distinguished experts in the relevant areas for additional evaluations in a double-blind manner. Based on their comments and recommendations, six papers were shortlisted as the finalists for the award. During the conference, the oral presentations of the six finalists will be further assessed by the DDCLS’20 Best Paper Award Committee. The winner of the "DDCLS Best Paper Award" will be selected by the committee after assessing the oral presentations. Furthermore, the interactive presentations of 75 papers in 2 poster sessions will be assessed by the DDCLS’20 Best Poster Award Committee during the conference, and one or two papers will be conferred to the "DDCLS Best Poster Award" by the committee after assessing the interactive presentations.

A U-disk containing the PDF files of all papers scheduled in the program and an Abstract Book will be provided at the conference to each registered participant as part of the registration material. The official conference proceedings will be published by the IEEE and included in the IEEE Xplore Database.

On behalf of the Technical Program Committee, we would like to thank all reviewers for giving time and expertise to provide comments, which are contributive to the Committee in making a fair decision on the acceptance/rejection of each paper. Thanks also go to the dedication, diligence, and commitments of the Invited Session Chairs Prof. Zengqiang Chen, Prof. Darong Huang, Prof. Jing Na, Prof. Fei Qiao, Prof. Senping Tian, Prof. Qinglai Wei, Prof. Zhanshan Wang, Prof. Jinpeng Yu, Prof. Weiwei Che, Prof. Yi Liu, and Prof. Jiayan Wen, Subject Session Chairs Prof. Zhihuan Song, Prof. Dongbin Zhao, Prof. Xin Xu, and all the members of the Technical Program Committee. We would like to gladly acknowledge the technical sponsorship provided by the Organizing Committee of DDCLS’20 and Technical Committee on Data Driven Control, Learning and Optimization, Chinese Association of Automation. We also convey our heartfelt thanks to friends, colleagues, and families who have helped us in completing the technical program directly or indirectly. Last but not least, we are grateful for the strong and enthusiastic support of all delegates, especially those old faces around the world.

We do hope that you will find your participation in DDCLS’20 in Liuzhou is really stimulating, rewarding, enjoyable, and memorable.

Mingxuan Sun Huaguang Zhang Technical Program Chair Technical Program Chair

DDCLS’20 Keynote Address

Keynote Address 1

Reinforcement Learning for Optimal Control Prof. Derong Liu Guangdong University of Technology, China

Saturday, Nov. 21, 2020 08:30-09:30 Dongcheng Hall / 东城厅 Abstract Reinforcement learning (RL) is one of the most important branches of artificial intelligence. Researchers have been using RL techniques in modern control theory. Self-learning control methodologies are a good representative of such efforts. RL recently has become a major force in the machine learning fields. On the other hand, adaptive dynamic programming (ADP) has now become popular in control communities. Both RL and ADP have roots in dynamic programming and in many ways they are equivalent. Major breakthroughs of ADPRL for optimal control were achieved around 2006, when iterative ADP approaches were introduced. The optimal control of nonlinear systems requires to solve the nonlinear Bellman equation instead of the Riccati equation as in the linear case. The discrete-time Bellman equation is more difficult to work with than the Riccati equation because it involves solving nonlinear partial difference equations. Though dynamic programming has been a useful computational technique in solving optimal control problems, it is often computationally untenable to run it to obtain the optimal solution, due to the backward numerical process required for its solutions, i.e., the well-known "curse of dimensionality". Self-learning optimal control based on ADPRL provides efficient tools for tackling the following two problems. (1) Nonlinear Bellman equation is solved using iterative ADP approaches which are shown to converge. (2) Neural networks are employed for function approximation in order to obtain forward numerical process. Some new developments in ADPRL for optimal control will be summarized.

Biography Derong Liu received the PhD degree in electrical engineering from the University of Notre Dame in 1994. He became a Full Professor of Electrical and Computer Engineering and of Computer Science at the University of Illinois at Chicago in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2015. He has published 19 books. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2105. He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, and a Fellow of the International Association of Pattern Recognition.

Keynote Address 2

Fully Autonomous UAS and Its Applications Prof. Ben M. Chen Chinese University of Hong Kong, China National University of Singapore, Singapore Saturday, Nov. 21, 2020 9:30-10:30 Dongcheng Hall / 东城厅 Abstract The research and market for the unmanned aerial systems (UAS), or drones, has greatly expanded over the last few years. It is expected that the currently small civilian unmanned aircraft market is likely to become one of the major technological and economic stories of the modern age, due to a wide variety of possible applications and added value related to this potential technology. Modern unmanned aerial systems are gaining promising success because of their versatility, flexibility, low cost, and minimized risk of operation. In this talk, we highlight some key techniques involved in developing fully autonomous unmanned aerial vehicles and their industrial application examples, which includes deep tunnel inspection, stock counting and checking in warehouses and building inspections.

Biography Ben M. Chen is currently a Professor in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. He was a Provost's Chair Professor in the Department of Electrical and Computer Engineering, the National University of Singapore (NUS), where he was also serving as the Director of Control, Intelligent Systems and Robotics Area, and Head of Control Science Group, NUS Temasek Laboratories. His current research interests are in unmanned systems, robust control and control applications. Dr. Chen is an IEEE Fellow. He has published more than 400 journal and conference articles, and a dozen research monographs in control theory and applications, unmanned systems and financial market modeling by Springer in New York and London. He had served on the editorial boards of several international journals including IEEE Transactions on Automatic Control and Automatica. He currently serves as an Editor‐in‐Chief of Unmanned Systems. Dr. Chen has received a number of research awards nationally and internationally. His research team has actively participated in international UAV competitions, and won many championships.

DDCLS’20 Keynote Address 3 Data and/or Control – Is Control Theory Becoming Obsolete? Prof. Frank Allgöwer University of Stuttgart, German Saturday, Nov. 21, 2020 11:00-12:00 Dongcheng Hall / 东城厅 Abstract While recent years have shown rapid progress of learning-based methods to effectively utilize data for control tasks, most existing control theoretic approaches still require knowledge of an accurate system model. It is worth asking if this trend towards data-driven approaches will ultimately lead to an obsolescence of classical systems and control theory. On the other hand, a key feature of control theory has always been its ability to provide rigorous theoretical guarantees – something that the learning community has only recently begun to address. In this talk, we present a novel framework for data-driven control theory, which does not rely on any model knowledge but still allows to give desirable theoretical guarantees. This framework relies on a result from behavioral systems theory, where it was proven that the vector space of all input-output trajectories of a linear time-invariant system is spanned by time-shifts of a single measured trajectory, given that the respective input signal is persistently exciting. We show how this result can be utilized to develop a mathematically sound approach to data-driven system analysis, with the possibility to verify input-output properties (e.g., dissipation inequalities) of unknown systems. Moreover, we propose a novel purely data-driven model predictive control scheme and we present theoretical results on closed-loop stability and robustness. Finally, the presented framework allows us to design state-feedback controllers with performance guarantees, even if the data are affected by noise.

Biography Frank Allgöwer is director of the Institute for Systems Theory and Automatic Control and professor in Mechanical Engineering at the University of Stuttgart in Germany. Frank's main interests in research and teaching are in the area of systems and control with a current emphasis on the development of new methods for data-based control, optimization-based control, networks of systems, and systems biology. Frank received several recognitions for his work including the IFAC Outstanding Service Award, the IEEE CSS Distinguished Member Award, the State Teaching Award of the German state of Baden-Württemberg, and the Leibniz Prize of the Deutsche Forschungsgemeinschaft. Frank has been the President of the International Federation of Automatic Control (IFAC) for the years 2017-2020. He was Editor for the journal Automatica from 2001 to 2015 and is editor for the Springer Lecture Notes in Control and Information Science book series and has published over 500 scientific articles. From 2012 until 2020 Frank also served a Vice-President of Germany's most important research funding agency the German Research Foundation (DFG).

Keynote Address 4

Data-Driven Model Reduction Prof. Alessandro Astolfi Imperial College London, UK University of Rome Tor Vergata, Italy Sunday, Nov. 22, 2020 8:30-9:30 VIP23 Hall / VIP23 厅 Abstract The aim of the talk is to discuss two methods for obtaining reduced order models, for linear and nonlinear systems, from data. In the first part of the talk the notion of moment for linear systems is generalized to nonlinear, possibly time-delay, systems. It is shown that this notion provides a powerful tool for the identification of reduced order models from input-output data. It is also shown that the canonical parameterization of the reduced order model as a rank-one update of the "interpolation-point matrix" is not necessary, hence one can prove robustness of data-driven model reduction algorithms against variations in the location of the interpolation points. In the second part of the talk the Loewner framework for model reduction is discussed and it is shown that the introduction of left- and right- Loewner matrices/functions simplifies the construction of reduced order models from data. This is joint work with Z. Wang (Southeast University), G. Scarciotti (Imperial College) and J. Simard (Imperial College).

Biography Alessandro Astolfi was born in Rome, Italy, in 1967. He graduated in electrical engineering from the University of Rome in 1991. In 1992 he joined ETH-Zurich where he obtained a M.Sc. in Information Theory in 1995 and the Ph.D. degree with Medal of Honor in 1995 with a thesis on discontinuous stabilisation of nonholonomic systems. In 1996 he was awarded a Ph.D. from the University of Rome "La Sapienza" for his work on nonlinear robust control. Since 1996 he has been with the Electrical and Electronic Engineering Department of Imperial College London, London (UK), where he is currently Professor of Nonlinear Control Theory and Head of the Control and Power Group. From 1998 to 2003 he was also an Associate Professor at the Dept. of Electronics and Information of the Politecnico of Milano. Since 2005 he has also been a Professor at Dipartimento di Ingegneria Civile e Ingegneria Informatica, University of Rome Tor Vergata. His research interests are focussed on mathematical control theory and control applications, with special emphasis for the problems of discontinuous stabilisation, robust and adaptive control, observer design and model reduction.

DDCLS’20

Distinguished Lecture 1

Data-Driven Wide-Range Nonstationary Process Monitoring Prof. Chunhui Zhao Zhejiang University, China.

Saturday, Nov. 21, 2020 13:00-14:00 VIP23 Hall / VIP23 厅 Abstract Modern industrial production often has wide-range nonstationary operating characteristics, such as batch manufacturing processes, wide-load power generation processes, etc. Due to its large-scale non-stationary operation characteristics, it raises new challenges to the safe and reliable operation of industrial processes and has become the focus of attention. Starting from the traditional batch process, this report will present the concept of a generalized batch process, analyze the specific characteristics of wide-range nonstationary industrial processes, and summarize the basic process monitoring techniques and the relevant research work in this field. It further analyzes the existing specific problems, and extend the traditional batch process analysis methods to industrial processes with wide-range non-stationary operation characteristics. Finally, the application of the proposed method in different fields will be briefly introduced.

Biography Chunhui Zhao has been a Professor with the College of Control Science and Engineering, Zhejiang University, Hangzhou, China. Her research interests include statistical machine learning and data mining for industrial application. She has authored or coauthored more than 120 papers in peer-reviewed international journals. She has published 2 monographs and authorized 21 invention patents. She has hosted more than 10 scientific research projects, including the NSFC funds, provincial projects and corporate cooperation projects. She was the recipient of the National Top 100 Excellent Doctor Thesis Nomination Award, New Century Excellent Talents in University, China, and the National Science Fund for Excellent Young Scholars, respectively. She has also obtained the first Automation Society Young Women Scientist Award, the Process Control Youth Award, etc., and is now an IEEE senior member. She has served AE of three International Journals, including Journal of Process Control, Control Engineering Practice and Neurocomputing, and two domestic journals, including Control and Decision, and Control Engineering.

11 Distinguished Lecture 2 Distributed Gradient Tracking for Optimization and Learning over Network Prof. Keyou You Tsinghua University, China Saturday, Nov. 21, 2020 14:00-14:30 VIP23 Hall / VIP23 厅 Abstract Many problems of recent interest in control and machine learning can be posed in the framework of mathematical optimization. As data gets larger and more distributed, distributed algorithms over networks offer ample opportunities to improve the speed and accuracy of optimization. In this talk, we shall exploit the distributed gradient tracking technique (DGT) to solve large-scale optimization and learning problems, e.g., the fully Asynchronous DGT which is easy to implement in directed networks with distributed datasets and robust to bounded transmission delays, while maintaining a linear convergence rate if local functions are strongly-convex with Lipschitz-continuous gradients. Moreover, we adopt the DGT to design distributed algorithms with explicit convergence rates for the distributed resource allocation and distributed training over networks, respectively. Experiments are included to show their advantages against the-state-of-the-art algorithms.

Biography Keyou You received the B.S. degree in Statistical Science from Sun Yat-sen University, Guangzhou, China, in 2007 and the Ph.D. degree in Electrical and Electronic Engineering from Nanyang Technological University (NTU), Singapore, in 2012. After briefly working as a Research Fellow at NTU, he joined Tsinghua University in Beijing, China where he is now an Associate Professor with tenure in the Department of Automation. He held visiting positions at Politecnico di Torino, The Hong Kong University of Science and Technology, The University of Melbourne and etc. His current research interests include networked control systems, distributed algorithms and learning, and their applications. Dr. You received the Guan Zhaozhi award at the 29th Chinese Control Conference in 2010, a CSC-IBM China Faculty Award in 2014, and the ACA Temasek Young Educator Award in 2019. He was selected to the National 1000-Youth Talent Program of China in 2014 and received the National Natural Science Fund for Excellent Young Scholars in 2017.

DDCLS’20

Distinguished Lecture 3

Prediction and Scheduling for Industrial Energy System Prof. Jun Zhao Dalian University of Technology, China

Saturday, Nov. 21, 2020 14:30-15:00 VIP23 Hall / VIP23 厅 Abstract Industrial energy resource saving is capable of not only improving the enterprise profits, but also carrying out the significant strategy meaning for our country. Given the fixed technical process and equipment, the optimization scheduling of the industrial energy system is the most important approach for such a goal. However, the most industrial energy systems exhibit a very complicated structure, which can hardly establish a mechanism based model to describe such a system, and the existing manual scheduling method makes the decision making process tardily. A class of data-driven predictive scheduling methodology is proposed. In detail, considering the consistent modeling, the quantitative uncertainty description, and the semantic characteristics of the energy data, the short-term prediction model, the prediction interval one and the long-term model are respectively reported, and a rolling optimization technique with the procedures of prediction-scheduling-validation is proposed. The mentioned approaches have been successfully applied to a number of industrial enterprises in our country.

Biography Jun Zhao is now the director of Intelligent Control Institute with the School of Control Science and Engineering, DUT, China. He has authored or co-authored over 100 technical publications in refereed journals and conference proceedings. He serves as associate editors for several top tier journals including Control Engineering Practice, IEEE TNNLS, Information Sciences, etc. From 2015, he became a Technical Committee member (TC6.2) of IFAC MMM society. In 2018, he obtained the First Class Prizes of Science and Technology Progress Award of CAA (Chinese Automation Association), and is now the scientist-in-chief of a National Key R&D Program of China. In addition, he was the recipient of Young Scholar of Yangtze River from Ministry of Education of China in 2016, and received the Excellent Young Scholar funding supported by National Natural Science Foundation of China in 2015. He is also the recipients of the Best Application Paper Award of WCICA2014, and the Zhang Zhongjun Best Paper Award of CPCC 2016.

13 Distinguished Lecture 4

Data Driven Brain Neurofiber Tract Identification Prof. Yuanjing Feng Zhejiang University of Technology, China

Saturday, Nov. 21, 2020 15:00-15:30 VIP23 Hall / VIP23 厅 Abstract Accurate brain neurofiber tract identification promises to have a high impact in fundamental neuroscience and its clinical applications. However, state-of-the-art fiber tracking algorithms are driven by local symmetrically orientation fields estimated from diffusion MRI, representing the local tangent direction to the white matter tract of interest. Conceptually, the principle of inferring connectivity with streamline prorogation from local symmetrically orientation fields can lead to problems as soon as pathways overlap, cross, branch, and have complex geometries. Usually, tractography-based connectome is dominated by lots of false-positive connections. This project will propose an asymmetric tensor stream-flow fiber tracking methods and its application in cranial nerve atlas reconstruction. Firstly, a stream-flow differential equation based on computational fluid mechanics will be presented for describing nerve fiber bundle. The asymmetric fiber geometries is expressed as the distribution of streamline cluster in tensor vector field which extends the streamline prorogation to more general stream-flow way. Then, an data driven automated neurofiber tract identification algorithm will be proposed for connectome-based cranial nerve tractographic atlas based on asymmetric global fiber tracking. Its potential clinical applications in neurosurgical planning and neurodegenerative diseases are presented.

Biography Yuanjing Feng holds a Ph.D in control science and engineering from Xi’an Jiaotong University, M.S. in Mechanical design and theory from Northwest A&F University. Currently, he is the Director of the Institute of Information Processing and Automation and is working as a professor at Zhejiang University of Technology. He is a visiting scholar from January 2010 to February 2012 and October 2018 to April 2019 in Laboratory of Mathematics in Imaging at Harvard University, where he worked with Professor Carl-Fredric Westin. To date, he has authored more than 40 peer-reviewed journal articles (including Automatica, Medical Image Analysis, NeuroImage, Brain research) and MICCAI, ISBI. His interests include data driven modeling and optimization in field of intelligence transportation system, medical image analysis.

DDCLS’20

Distinguished Lecture 5

Non-Intrusive Modeling for We-Energy based on Mechanism-Data Hybrid Drive Prof. Qiuye Sun Northeastern University, China

Sunday, Nov. 22, 2020 9:50-10:20 VIP23 Hall / VIP23 厅 Abstract Generally, an accurate model can describe the operating status of a system more effectively and provide a more reliable theoretical basis for the system optimization and control. To distinguish from the traditional invasive modeling, a non-invasive modeling method based on mechanism and data hybrid is proposed for we-energy, a typical energy system. By using this method, non-invasive modeling for the energy system including photovoltaic, wind power, energy storage devices and energy coupling devices can be carried out. Firstly, the meteorological data, energy output and price curve are utilized to analyze and extract the characteristic of we-energy, and then the characteristic database is established. Afterwards, by taking the port energy data of we-energy as the random noise input, the GAN generator is improved and more applicable to we-energy characteristic. The feedback evaluation of GAN discriminator is utilized to guide the generator model, and we-energy model is established by the output of the discriminator. This model can accurately demonstrate the static and dynamic characteristic of the terminal integrated energy unit, laying a foundation for the collaborative optimization of the integrated energy system.

Biography Qiuye Sun (M’11) received the M.S. degree in power electronics and drives and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China. He is currently a full Professor with Northeastern University and obtained Special Government Allowances from the State Council in China. He has authored or coauthored over 200 papers, authorized over 100 invention patents, and published over 10 books or textbooks. He is an Associate Editor of IEEE TNNLS, IEEE Access, IEEE/CAA Journal of Automatica Sinica, CSEE Journal of Power and Energy Systems, IET Cyber-Physical Systems, Journal of Control and Decision, Mathematical Problems in Engineering. His current research interests include optimization analysis technology of power distribution network, network control of Energy Internet, Integrated Energy Systems and Microgrids.

15 Distinguished Lecture 6

Dynamic Reference Programming-Based Model Predictive Pattern Control by Dynamic Controlled PCA Prof. Xiaoli Luan Jiangnan University, China

Sunday, Nov. 22, 2020 10:20-10:50 VIP23 Hall / VIP23 厅 Abstract A dynamic controlled principal component analysis (DCPCA) algorithm is proposed to extract desirable latent variables and construct the pattern space of industrial process from a set of measured variables. The constructed pattern space contains the most variations of process variables caused by free motion, as well as the forced movement subjected to the causality originating from control inputs. Consequently, the pattern can characterize the process running state maximally and comprehensively with the minimum dimensions, and the pattern motion equation can be identified to describe the dynamic behavior of industrial plant. After that, a dynamic reference programming-based MPC is designed to drive pattern to track the optimal operation point with zero steady-state error if the target is reachable, otherwise the pattern is steered to a suboptimal but closest position to the target. This MPC strategy is characterized by parameterized reference inputs and enlarged terminal constraint set derived from null space analysis, which could guarantee the maximum reachable optimization area is lossless when solving the objective function.

Biography Xiaoli Luan received the B.Sc. degree in industrial automation from Jiangnan University, China, in 2002; the M.Sc. degree in control theory and control engineering from Jiangnan University, China, in 2006; and the Ph.D. degree in control theory and control engineering from Jiangnan University, China, in 2010. Now she is a professor of the Institute of Automation, Jiangnan University. In 2016, she was a Visiting Professor with the University of Alberta, Canada. Her research interests include robust control and optimization of complex nonlinear systems.

DDCLS’20

Distinguished Lecture 7

Recent Advances in Remaining Useful Life Prediction and Health Management Technology Prof. Xiaosheng Si Henan Polytechnic University, China

Sunday, Nov. 22, 2020 10:50-11:20 VIP23 Hall / VIP23 厅 Abstract Stochastic degradation data analysis is the basic and core component to implement life prognosis and health management of complex engineering systems. Extensive studies on this subject have been witnessed in the fields of reliability and system engineering. This report will be focused on challenging and fundamental problems in data modeling and model solution for the remaining useful life prediction of stochastic degrading systems. The emphasis will be placed on techniques dealing with linear models, nonlinear model, and switching models. Finally, the future directions will be discussed.

Biography Xiaosheng Si received the B. Eng., M. Eng., and Ph.D. degrees from the Department of Automation, Rocket Force University of Engineering, Xi’an, China, in 2006, 2009, and 2014, respectively, all in control science and engineering. He is currently a Professor in control science and engineering with the Rocket Force University of Engineering. He has authored or co-authored more than 50 articles in several journals including European Journal of Operational Research, IEEE Transactions on Industrial Electronics, IEEE Transactions on Reliability, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man and Cybernetics—Part A, IEEE Transaction on Automation Science and Engineering, Reliability Engineering and System Safety, and Mechanical Systems and Signal Processing. He is an active reviewer for a number of international journals. His research interests include evidence theory, expert system, prognostics and health management, reliability estimation, predictive maintenance, and lifetime estimation. Dr. SI is an Associate Editor of IEEE ACCESS.

17 Distinguished Lecture 8

Iterative Learning Control with Incomplete Information Prof. Dong Shen Renmin University of China, China

Sunday, Nov. 22, 2020 11:20-11:50 VIP23 Hall / VIP23 厅 Abstract Iterative learning control is an effective control strategy for repetitive systems by utilizing the input and output information of the previous iterations. It has been shown advantageous in dealing with high nonlinearity and complexity while achieving good tracking performance of high precision. In this talk, we will report recent advances in iterative learning control with incomplete information. Here, incomplete information is generally caused by various practical issues such as data dropout, quantization, varying trial lengths, and communication constraints. The control design and analysis under these issues will be elaborated.

Biography Dong Shen received the B.S. degree in mathematics from Shandong University, Jinan, China, in 2005. He received the Ph.D. degree in mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing, China, in 2010. From 2010 to 2012, he was a Post-Doctoral Fellow with Institute of Automation, CAS. From 2016 to 2017, he was a visiting scholar at National University of Singapore. From 2019 July to August, he was a visiting scholar at RMIT University. From 2012 to 2019, he was with College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. Since Dec 2019, he has been a Full Professor with School of Mathematics, Renmin University of China, Beijing, China. His current research interests include iterative learning control, stochastic control and optimization, machine learning and its applications. He has published more than 110 refereed journal and conference papers. He is (co-)author of four monographs, published by Springer, Wiley, and Science Press, respectively. Dr. Shen received IEEE CSS Beijing Chapter Young Author Prize in 2014. He is a Senior Member of IEEE.

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS’20)

Technical Program and Book of Abstracts

DDCLS’20

A Variable Parameter Model-Free Adaptive Control Saturday, 21 November, 2020 Algorithm and Its Application in Distillation Tower SatA01 Room 1 System Data driven control 13:30-15:30 Yu Feng Tianjin Univ. Chair: Na Dong Tianjin Univ. Na Dong Tianjin Univ. CO-Chair: Quan Quan Beihang Univ. Yongzhou Li Tianjin Univ. Wenjin Lv Tianjin Univ. 13:30-13:50 SatA01-1 Data Driven Control for a Class of Nonlinear Systems In order to achieve better control performance of with Stochastic Fading Channels chemical process, model free adaptive control (MFAC) Wei Yu Henan Polytechnic Univ. scheme is improved by adding two new parameters L1, Xuhui Bu Henan Polytechnic Univ. L2, furthermore to apply in distillation tower system. Yanling Yin Henan Polytechnic Univ. Compared with basic MFAC, the number of parameters Jiaqi Liang Henan Polytechnic Univ. in this novel method is reduced and variable. Firstly, nonlinear system with time-varying desired output is This paper investigates the data driven model free used to carry out numerical simulation for the sake of adaptive control (MFAC) problem for a class of verifying the effectiveness of this algorithm. After that, non-affine nonlinear systems with stochastic fading the improved MFAC algorithm is applied to the control of channels. Firstly, the phenomenon of signal fading is the distillation tower system, and the result fully regarded as an independent stochastic process demonstrates the proposed algorithm has strong occurring at the output side, which has known stability, fast tracking speed. At last, for many systems mathematical expectations. Using an innovative with time delay in chemical process, such as distillation linearization method, the considered non-affine system tower system, a set of validated control method is converted into a linear model with a time-varying frameworks is proposed in this paper. It is expected to parameter called PPD and the MFAC controller is be universally popularized and applied to the control of redesigned by utilizing the faded outputs. The stability of chemical process. the system is analyzed rigorously and the influence of incomplete signal transmission on system convergence 14:30-14:50 SatA01-4 is explored. Finally, a numerical example shows the A Method for Analyzing the State Controllability of validity of the presented strategies. Linear Discrete Time-varying Time-delay Systems Zhuo Wang Beihang Univ. 13:50-14:10 SatA01-2 Beijing Academy of Quantum Information Sci. Data-Driven Stability Margin for MIMO Systems Qi Yuan Beihang Univ. Jinrui Ren Beihang Univ. Quan Quan Beihang Univ. The state controllability of time-delay systems is important for a wide range of scientific and industrial The notion of stability margin (SM) plays an important processes. However, few researches up to now have role in control engineering. For multiple-input been carried out for extensive studies on this problem. multiple-output (MIMO) systems, the classic SM is no This paper develops a method for analyzing the state longer applicable. Although some robust SM analysis controllability of linear discrete time-varying time-delay methods are popular among multivariable systems, they systems. By establishing an augmented state-space are model-based, or not easy-to-use in engineering model of the original system, only a few parameters are sometimes. In this paper, L2 gain margin and L2 needed to complete the determination of the state time-delay margin are defined for linear MIMO systems, controllability, which greatly reduces the amount of and a corresponding SM analysis method is proposed calculation. Then, a specific example is presented to by utilizing a loop transformation and the small-gain show the effectiveness of the proposed analysis theorem. Most importantly, a data-driven method for method. measuring the defined SMs is also presented. As a frequency-domain method, this method can be used to 14:50-15:10 SatA01-5 obtain the SMs of MIMO systems experimentally on Model Free Adaptive Pitch Control of a Flapping Wing model-free occasions. The proposed SM analysis and Micro Aerial Vehicle with Input Saturation measurement method is simple and practical. Simulation Tianhe Wang Beijing Jiaotong Univ. and experiment are given to illustrate the effectiveness Shangtai Jin Beijing Jiaotong Univ. and practicability of the proposed method. Zhongsheng Hou Beijing Jiaotong Univ. Qingdao Univ. 14:10-14:30 SatA01-3 In this paper, the dynamics of a ﬂapping wing micro aerial vehicle is analyzed. Aiming at the difﬁculty of

21 controller design caused by the nonlinearity, signals directly instead of the unknown nonlinear time-varying and strong coupling characteristics of the functions. Moreover, a linear state observer is designed micro aerial vehicle, a full form dynamic linearization for estimating the unmeasured states. Based on the based model free adaptive control scheme (FFDL-MFAC) backstepping technique, a novel adaptive MTN control is presented to realize the pitch control of the controlled strategy with simple structure and good real time vehicle. In addition, a compensator is introduced to property is proposed. The designed controller can overcome the control input saturation caused by the guarantee all the signals of the closed-loop system are limitation of the actuator. Simulation results are provided bounded and the tracking error converges to a small to demonstrate the effectiveness of the proposed MFAC. neighborhood of the origin. Simulation results are given to demonstrate the effectiveness of the proposed 15:10-15:30 SatA01-6 method. Virtual Constraint Force Control for Teleoperation System of Live-Power Line Maintenance 13:50-14:10 SatA02-2 Xia Liu Xihua Univ. PH Control of Slurry in Wet Flue Gas Desulfurization Chengwei Pan Univ. of Electronic Sci. & Tech. of China Based on Model Free Adaptive Control Yong Chen Univ. of Electronic Sci. & Tech. of China Jian Liu Beijing Univ. of Tech. Xiaoli Li Beijing Univ. of Tech. In order to reduce the risk of the human operator in Yang Li Communication Univ. of China live-power line maintenance while performing the maintenance tasks accurately and efficiently, a virtual In limestone-gypsum wet flue gas desulfurization constraint force control strategy for robotic process, the pH change process of slurry in absorption teleoperation system is proposed. The virtual constraint tower has the characteristics of high nonlinearity, large force is generated by the virtual spring which is lag and various disturbances. According to the process feedback to the operator’s hand by the master. The of pH control in wet flue gas desulfurization, a model motion of the operator's hand can be constrained within free adaptive control algorithm based on compact form the desired range near the target point and meanwhile, dynamic linearization (CFDL-MFAC) is designed. Then the human operator can have the sense of touch from the simulation is carried out with Matlab based on the slave. The proposed control strategy is verified by hammestein model of slurry pH control system. It is three experiments on live-power line maintenance tasks turned out that CFDL-MFAC algorithm can effectively including clamping porcelain insulator pin, clamping use the input and output data of the pH control process drop-out fuse insurance, and clamping object on to realize the tracking control of slurry pH and obtain overhead wires. The results show that compared to the high control accuracy, which verifies the effectiveness of traditional method, the proposed control strategy can the method. Compared with PID control, CFDL-MFAC save the task execution time of live-power line controller can not only obtain better control effect, but maintenance, reduce the position tracking error between also effectively suppress external disturbance. the master and the slave and improve the stability of the system. 14:10-14:30 SatA02-3 SatA02 Room 2 Event-Triggered Consensus Output Tracking Strategy Model-free adaptive control 13:30-15:30 for Multiagent Systems Utilizing Model-Free Adaptive Chair: Xiaoli Li Beijing Univ. of Tech. Control CO-Chair: Jian Feng Northeastern Univ. Weizhao Song Northeastern Univ. Jian Feng Northeastern Univ. 13:30-13:50 SatA02-1 Observer-Based Adaptive Multi-dimensional Taylor In this article, a model-free-adaptive-control-based Network Control for Nonlinear Systems with Time-Delay (MFAC-based) event-triggered (ET) consensus output Lei Chu Qingdao Univ. of Sci. & Tech. trackin g problem for multiagent systems (MASs) is Shuhua Zhang Qingdao Univ. of Sci. & Tech. investigated. The dynamic models of agents are Mingxin Wang Qingdao Univ. of Sci. & Tech. unknown, and only a subset of agents can acquire the Shanliang Zhu Qingdao Univ. of Sci. & Tech. reference trajectory. The consensus tracking algorithm Yuqun Han Qingdao Univ. of Sci. & Tech. is designed by the real-time input/output data and Key Laboratory of Measurement & Control pseudo-partial-derivative (PPD), which is an important of Complex Systems of Engineering parameter of MFAC approach. An output observer is built to design the centralized ET mechanism. Then, the In this paper, an observer-based adaptive boundedness analysis that the tracking error is Multi-dimensional Taylor network (MTN) controller is uniformly ultimately bounded (UUB) is given. Finally, a proposed for strictly feedback nonlinear systems with simulation experiment is provided to verify the feasibility time-delay and unmeasurable states. MTNs are utilized of the ET consensus output tracking strategy for MASs. to approximate the unknown and desired control input

DDCLS’20

14:30-14:50 SatA02-4 Tech. Model-Free Adaptive Control Based on Neural Network Xiongxiong He Zhejiang Univ. of Tech. Observer for the Chaotic Power Supply System Haiyue Zhu Singapore Institute of Manufacturing Ao Bai Northeastern Univ. Tech. Yanhong Luo Northeastern Univ. Yuanjing Feng Zhejiang Univ. of Tech. Huaguang Zhang Northeastern Univ. Qiang Chen Zhejiang Univ. of Tech. Zhengyang Zhu Zhejiang Univ. of Tech. In this paper, we considered a type of chaotic power Xiaocong Li Singapore Institute of Manufacturing supply system and presented a neural network adaptive Tech. method with Neural Network Observer (NNO). First, the mathematical model of the chaotic power supply system Overhead cranes, which are typically underactuated, are is summarized. Then aiming for the unknown model of studied systematically nowadays. While, the model n-order nonlinear system, the controller is designed by widely used in research is ideal. Thus, the the neural network adaptive method. There is no need to corresponding controllers may react badly under know the accurate mathematical model and state external disturbances, unmodeled dynamics and input information of the controlled object. We estimate the constraints. To tackle this issue, this paper develops an state information and model information of the adaptive version of anti-sway trajectory tracking controlled object through the input and output data of controller for overhead cranes. First, as to constrained the object first, and use the obtained estimation results input, we perform a mapping action from the system to implement the controller, and give the corresponding input to the hyperbolic tangent function. Then adaptation theoretical analysis. Finally, the effectiveness of the mechanisms are proposed to adjust the modified inputs designed controller is verified by simulation of a power and the system uncertainty. Such a controller achieves system with chaotic motion. precise positioning and swing suppression despite input saturation, system uncertainty and external 14:50-15:10 SatA02-5 disturbances. The crane system proves to be dissipative Model Free Adaptive Control for the Temperature with the proposed controller. The experiments Adjustment of UGI Coal Gasification Process in accomplished on a laboratory-size bridge crane reveal Synthetic Ammonia Industry that the proposed controller asymptotically stabilizes all Shida Liu North China Electric Power Univ. system states. Jiao Sun North China Electric Power Univ. SatA03 Room 3 Honghai Ji North China Electric Power Univ. Data-driven fault diagnosis and health maintenance (I) Zhongsheng Hou Qingdao Univ. Lingling Fan Beijing Information Sci. & Tech. Univ. 13:30-15:30 Chair: Jie Ma Beijing Information Sci. & Tech. Univ. In this manuscript, a data-driven model free adaptive CO-Chair: Guo Xie Xi’an Univ. of Tech. control (MFAC) method is introduced for a UGI gasifier. During the UGI gasification process, the temperature of 13:30-13:50 SatA03-1 crude gas inside the UGI gasifier is very important. Fault Diagnosis of Rolling Bearing Based on Improved However, the accurate multi-input and multi-output LeNet-5 CNN mathematical model describing the dynamics of the Siyu Li Xi’an Univ. of Tech. crude gas temperature cannot be created due to the Guo Xie Xi’an Univ. of Tech. complexity of the gasification systems. The main feature Wenjiang Ji Xi’an Univ. of Tech. of MFAC method is that the controller design depends Xinhong Hei Xi’an Univ. of Tech. only on the input and the output measurement data of Wenbin Chen1 Xi’an Univ. of Tech. the controlled plant. Specifically, the MFAC controller is designed via a novel dynamic linearization technique To solve the problem of fault diagnosis of rolling bearing with a time varying parameter termed Poseudo-Jacobian caused by large amount of data and difficulties of Matrix (PGM), which contains the coupling information processing those data on to bearing set, based on of each output variable. Further, simulation results show Convolution Neural Network, a new method of data that MFAC has a very reliable tracking ability for the processing is proposed in this paper. With this method, temperature adjustment of the gasification process. one-dimensional time domain signal can be transformed into two-dimensional images, which is more suitable for 15:10-15:30 SatA02-6 Convolutional Neural Network processing. Meanwhile, Adaptive SMC-based Trajectory Tracking Control of the traditional machine learning method has the Underactuated Overhead Cranes disadvantage of low robustness and low recognition rate with noise interference. Therefore, based on the feature Shengzeng Zhang Zhejiang Univ. of Tech. extraction of Convolution Neural Network, in this paper Singapore Institute of Manufacturing we proposed an improved LeNet-5 Convolution Neural 23

Network model, that is, adding a convolution layer and a based contribution plot is that they are affected by the pooling layer to the classic LeNet-5 model. The hidden so called smearing effect. In fact, industrial process layer features are extracted by using the trainable variables can be classified into groups according to their convolution kernel, while the extracted implicit features correlation or process structure, hence it is are reduced by the pooling layer, the Soft max classifier straightforward to consider the group-wise fault is used for classification and recognition of rolling isolation problem. This paper introduces the sparse bearing faults. In this paper we verified the effectiveness group Lasso as a regularization method to improve the of the improved LeNet-5 model for fault diagnosis of fault isolation ability of PCA based contribution plot. The rolling bearing by using the rolling bearing data to train sparse group Lasso term considers both group-wise the classic LeNet-5 model and the improved model. sparsity and within-group sparsity. Hence more accurate diagnosis can be obtained. In order to solve the 13:50-14:10 SatA03-2 optimization problem of sparse group Lasso, an efficient Satellite MicroAnomaly Detection Based on Telemetry algorithm based on ADMM (Alternating Direction of Data Method of Multipliers) is proposed. Application study to Chao Sun 63758 Unit of PLA the Tennessee Eastman (TE) process shows that the Mingzhang E 63758 Unit of PLA proposed method can better isolate faulty variables than Ying Du Guangdong Univ. of Petrochemical competitive methods. Tech. Chuanmin Ruan 63758 Unit of PLA 14:30-14:50 SatA03-4 Feature Extraction of Rolling Bearing Faults Based on The military requirements of space security defense and VMD and FRFT space fast response are increasingly urgent. Accurate Lei Jiao Beijing Information Sci. & Tech. Univ. and effective micro anomaly detection of on-orbit Jie Ma Beijing Information Sci. & Tech. Univ. satellites is an important technical way of satellites life cycle health management. Under this military The fault signal of rolling bearing is non-stationary background, the micro anomaly detection of the key nonlinear signal, and it is difficult to extract the feature components of the satellite is proposed and carried out. of weak fault under strong background noise. This paper In order to solve the problems of low diagnostic uses a new filtering method-Fractional Fourier accuracy of the traditional Voherra series model in Transform (FRFT). Compared with the traditional Fourier satellite telemetry signal micro anomaly detection, transform (FFT), it can make the time-frequency o-Voherra series anomaly detection model for the feature characteristics of unstable fault signals better displayed extraction of telemetry data based on the optimized and suppress cross-interference. In this paper, the sequence model is proposed. Firstly, the feature of method of feature extraction of rolling bearings satellite telemetry data is extracted by using the combined with Variational Mode Decomposition (VMD) constructed optimized sequence model. Secondly, and Fractional Fourier Transform (FRFT) is used. First, phase space reconstruction of telemetry data after the original vibration signal is decomposed by VMD to preprocessing and feature extraction. Finally, the obtain several intrinsic mode component functions telemetry data micro anomaly detection are realized by (IMF). The component with the largest correlation the proposed o-Voherra series model. Through the coefficient is selected as the optimal component for remote sensing data experiment of the key components filtering in the fractional order domain. The of the satellite after desensitization, the proposed model 1.5-dimensional envelope spectrum of the filtered signal can accurately realize the micro anomaly detection of is analyzed. The frequency value corresponding to the the key components of the satellite. maximum amplitude can be obtained. This frequency value is the fault characteristic frequency of the rolling 14:10-14:30 SatA03-3 bearing. The simulation results show that the method Improved PCA-based Fault Isolation using Sparse Group can effectively extract the fault characteristic Lasso information of the rolling bearing. Wei Chen China Jiliang Univ. Jiusun Zeng China Jiliang Univ. 14:50-15:10 SatA03-5 Jiangxi Univ. of Finance & Economics Bearings Remaining Useful Life Prediction with Yifan Li Jiangxi Univ. of Finance & Economics Combinatorial Feature Extraction Method and Gated Shihua Luo Jiangxi Univ. of Finance & Economics Recurrent Unit Network Li Xiao Wuhan Univ. of Sci. & Tech. In industrial process control, data-driven fault detection Zhenxing Liu Wuhan Univ. of Sci. & Tech. and isolation methods have developed rapidly due to the Yong Zhang Wuhan Univ. of Sci. & Tech. easy availability of large amount of data. In fault Ying Zheng Huazhong Univ. of Sci. & Tech. isolation, principal component analysis (PCA) based contribution plot is a standard tool. The problem of PCA Remaining useful life (RUL) prediction is one of the most

DDCLS’20 important technologies to implement the health the large topological network is divided into different management and predictive maintenance of rotating subnetworks. When the system is running dynamically, machinery. To predict precisely the RUL, a three-stage the Manhattan distance between the sub networks can strategy is proposed. Firstly, twenty-four basic reflect the running state of the system. Based on the characteristics are extracted from vibration signal, distance of sub networks, the similarity of sensor which are reconstructed by combining those basic networks with continuous changes is calculated and the characteristics with complete ensemble empirical mode similarity correlation sequence is formed. Through the decomposition with adaptive noise (BC-CEEMDAN), and matching between the similarity correlation sequence then the trend curves are extracted to reduce the and the historical experience sequence, we can judge fluctuation. Next, the most sensitive features are whether the system dynamic condition is abnormal. By selected by employing a linear combination of the simulation experiment data, we verify the monotonicity and correlation criteria. Finally, by input effectiveness of the proposed method. the selected features into the gated recurrent unit (GRU) SatA04 Room 4 neural network, we achieve the efficient health indicator IS：RNN for computing and its robotic applications with BC-CEEMDAN-GRU. To verify the effectiveness of 13:30-15:30 the proposed approach, experiment on PRONOSTIA Chair: Long Jin Lanzhou Univ. bearing datasets is carried out, and the advantage is CO-Chair: Shan Liu Zhejiang Univ. emphasized by comparison with the six existing methods. 13:30-13:45 SatA04-1 15:10-15:30 SatA03-6 Discrete-time recurrent neural network for solving discrete-form time-variant complex division Sensor Correlation Network Based Anomaly Detection Zhenggang Pan Yangzhou Univ. for Thermal Systems on Ships Wei Zheng Sci. & Tech. on Thermal Energy & Dimitrios K. Gerontitis Aristotle Univ. of Thessaloniki Power Laboratory Jian Li Xinyang Normal Univ. China State Shipbuilding Corp. Ltd. Hongkuan Zhou Sci. & Tech. on Thermal Energy & In recent years, recurrent neural network (RNN) model Power Laboratory has been widely investigated for time-variant problems. China State Shipbuilding Corp. Ltd. In this paper, we focus on discrete-form time-variant Zhiqiang Qiu Sci. & Tech. on Thermal Energy & complex division solving. Firstly, based on the Power Laboratory traditional Euclid division, we present the problem China State Shipbuilding Corp. Ltd. formulation of time-variant complex division. Then, in Zhiwu Ke Sci. & Tech. on Thermal Energy & the continuous-time environment, time-variant complex Power Laboratory division is converted a simple time-variant matrix vector China State Shipbuilding Corp. Ltd. equation equivalently; correspondingly, discrete-form time-variant complex division is converted a Mo Tao Sci. & Tech. on Thermal Energy & discrete-form time-variant matrix vector equation. Power Laboratory Secondly, we present different discretization formulas China State Shipbuilding Corp. Ltd. and corresponding different discrete-time recurrent Zhaoxu Chen Sci. & Tech. on Thermal Energy & neural network (DTRNN) models that have different Power Laboratory accuracy for solving the discrete-form time-variant China State Shipbuilding Corp. Ltd. matrix vector equation. Finally, comparative numerical experimental results are conducted to prove the In this paper, we propose an approach to handle the effectiveness of the DTRNN models for solving anomaly detection for the thermal system on ships by discrete-form time-variant complex division. the sensor associated network method. A large number of sensors are placed in different positions of the 13:45-14:00 SatA04-2 thermal system. These sensors form a topological A Long Short Term Memory Network Based on Surface network which can represent the operation state of the Electromyography for Continuous Estimation of Elbow thermal system. There are both linear correlation and Joint Angle nonlinear correlation between the operating parameters Yuanyuan Chai Changchun Univ. of Tech. reflected by these sensors. The MAS index from MINE is Keping Liu Changchun Univ. of Tech. utilized to represent the correlation information between Zhongbo Sun Changchun Univ. of Tech. sensors when the thermal system is in dynamic Jilin Univ. operation condition. Based on the MAS correlation Gang Wang Changchun Univ. of Tech. coefficient, the sensor correlation network is Tian Shi Jilin Univ. constructed to represent the dynamic operation process of thermal system. Using DBSCAN clustering algorithm, A simple long short term memory (LSTM) network is

25 built to estimate the model which is described the the processed signals as input, the WNN model is relationship between the elbow joint angle and the trained to estimate the knee-joint angles in continuous surface electromyography (sEMG) signals in this paper. motion. To validate the effectiveness of the WNN model, The sEMG time series of biceps and triceps are the one able-bodied person sit in a chair and accomplish leg inputs of the model, and the elbow joint angle is the stretching in the experiment, and simultaneously record output of the model. The sEMG signals while the user is the sEMG signals from the vastus rectus (VR) and the performing flexion and extension movements are angles of the knee joint. Then, the estimation results of collected by Biopac. Elbow joint angle is measured by the WNN model are compared with the RBF neural angle sensor. The results show that for simple flexion network and the BP neural network. The experimental and extension movements, the model is able to eatimate results show that the WNN model has the best the movement intention of the elbow. performance in the knee-joint angles estimation than the other two neural network models. The root mean square 14:00-14:15 SatA04-3 (RMS) error of the knee-joint angles is 6.5054◦ and the Kinematics Analysis of 7-DOF Upper Limb Rehabilitation time is 5.3271 seconds. The proposed method can be Robot Based on BP Neural Network applied to rehabilitation robots or assisted exoskeleton. Zaixiang Pang Changchun Univ. of Sci. & Tech. Changchun Univ. of Tech. 14:30-14:45 SatA04-5 Tongyu Wang Changchun Univ. of Sci. & Tech. On Welding Trajectory Centerline Extraction Based on Shuai Liu Changchun Univ. of Tech. Fuzzy Neural Network Zhanli Wang Changchun Univ. of Tech. Rong Bai Changchun Univ. of Tech. Linan Gong Changchun Vocational Institute of Tech. Changchun Decent Opto-Electronic Tech. Co., Ltd. To solve the inverse kinematics problem of 7-DOF upper Shuaishi Liu Changchun Univ. of Tech. limb rehabilitation training robot, propose a new solution Taiting Liu Changchun Univ. of Tech. method based on BP neural network. Taking a 7-DOF upper limb rehabilitation training robot as the research Based on the advantages of visual sensing technology object, carry out the forward kinematics analysis, with abundant image information and high measurement establish the BP neural network model for solving the accuracy, this paper studies the method of extracting the inverse kinematics and improve the neural network. centerline of the welding trajectory. The image was Finally, MATLAB is used to simulate and verify, the preprocessed to obtain the grayscale image of the weld, simulation results show that the improved BP neural the edge of the weld was detected by fuzzy neural network model can solve the inverse kinematics of network, the contour image of the weld was obtained 7-DOF upper limb rehabilitation training robot, avoid the iteratively by morphological image processing, and the complex problem of traditional inverse solution center line of the weld trajectory was extracted by calculation, and the solution process is simple; Hessian matrix. It is verified by experiments that the compared with the standard BP neural network, the centerline extraction method of weld trajectory studied learning convergence speed is faster and the solution in this paper can accurately extract the centerlines of precision is higher, so it is a feasible 7-DOF inverse welding trajectories of different shapes. kinematics solution method for upper limb rehabilitation training robot. 14:45-15:00 SatA04-6 Power-sum Function Activated Recurrent Neural 14:15-14:30 SatA04-4 Network Model for Solving Multi-linear Systems with Continuous Estimation of Human Knee-Joint Angles Nonsingular M-tensor from SEMG Using Wavelet Neural Network Shuqiao Wang Qinghai Normal Univ. Wanting Li Changchun Univ. of Tech. Xiujuan Du Qinghai Normal Univ. Keping Liu Changchun Univ. of Tech. Academy of Plateau Sci. & Sustainability Zhongbo Sun Changchun Univ. of Tech. Jilin Univ. Recurrent neural network (RNN), as a branch of artificial Gang Wang Changchun Univ. of Tech. intelligence, shows powerful abilities to solve the Feng Li Changchun Univ. of Tech. complicated computational problems. Due to the Xin Zhang Changchun Univ. of Tech. similarity between solving equations and controlling Yanpeng Zhou Changchun Univ. of Tech. dynamic systems, RNN-based approaches can also be analysed from the perspectives of control. Multi-linear Surface electromyography (sEMG) signals contain a systems, on the other hand, are a type of tensor wealth of information associated with human’s equations with considerable complexity due to the movement. In this paper, a wavelet neural network special structure of tensors. In this paper, a power-sum (WNN) model is proposed and implemented to estimate function activated RNN model is proposed to find the human knee-joint angles from the sEMG signals. With solutions of the multi-linear systems with nonsingular

DDCLS’20

M-tensors. It is theoretically proved that the proposed Chair: Bing Song East China Univ. of Sci. & Tech. RNN model is stable in the sense of Lyapunov stability theory and converges to the theoretical solution. In CO-Chair: Guanbin Gao Kunming Univ. of Sci. & Tech. addition, computer simulations are provided to substantiate the effectiveness and superiority of the 13:30-13:45 SatA05-1 proposed RNN model. An ESN based Modeling for Roll-to-Roll Printing Systems 15:00-15:15 SatA04-7 Zhihua Chen Guangzhou Univ. Temporal Convolutional Network Based Short-term Load Forecasting Model Tao Zhang Huazhong Univ. of Sci. & Tech. Kaiming Gu Shanghai Univ. Zheng Zhang Guangzhou Univ. Li Jia Shanghai Univ. In this paper, a modeling scheme based on Echo State Load forecasting has always been the focus of energy Networks (ESN) is designed and discussed for modeling management system research. Recently, with the in Roll-to-Roll (R2R) systems. R2R system involves development of machine learning and artificial transport and process of thin, flexible, continuous intelligence technology, more and more models are materials (called webs). An accuracy model is critical to applied to load forecasting. In this paper, we design a the research of R2R system, such as model-based model based on the temporal convolutional network for control and prediction. Existing mechanism modeling short-term load forecasting, which can accurately methods currently used in R2R systems require complex capture the feature form historical load data. Combine derivation and do not provide the accuracy performance the actual load data collected from a certain region of for changing operating conditions and material Shanghai, we compare our model with three traditional properties. The modeling scheme based on ESN utilizes models, including ARIMA model, ANN model, and LSTM the nonlinear approximation approach where the optimal model. The experiment results show that the model output connect weights of the network are calculated proposed in this paper achieves the best performance based on matching of the actual closed-loop R2R and has superior accuracy in short-term load printing system. The model established by the proposed forecasting. method considers the effect of operating conditions and material properties. Experimental data from an industrial 15:15-15:30 SatA04-8 printing system is used to corroborate the accuracy of RGBD Object Recognition and Flat Area Analysis R2R system model can be raised double by the Method for Manipulator Grasping proposed method which compared with mechanism Kaijun Wang Zhejiang Univ. modeling methods. Shan Liu Zhejiang Univ. 13: 45-14:00 SatA05-2 Backstepping Sliding Mode Maneuvering Control for a Grasping guided by visual recognition and positioning is Class of Surface Ships a practical requirement of discrete automation. This Jie Ma Dalian Maritime Univ. paper proposes a general method of using RGBD image Junsheng Ren Dalian Maritime Univ. recognition, which can recognize object with only one Weiwei Bai Guangdong Univ. of Tech. RGB template photograph of the target object, calculate Hongyi Li Guangdong Univ. of Tech. 3D coordinates and pose, and guide the 6 DOF manipulator to grasp object. SIFT(Scale Invariant This paper investigates the maneuvering problem for a Feature Transform) is used to extract feature points of class of surface ships in the presence of wave template image and real-time scene image and complete disturbance. The maneuvering problem involves the matching. Matched feature points are used as seed geometric task and the speed assignment along the points to segment target objects in depth image. This path. In order to solve the maneuvering problem, a method is fast and requires less prior knowledge. In backstepping sliding mode controller are designed order to optimize the grasping, this paper uses the under the wave influence. By establishing the Shape Index method to locate the flat area on the object conversing law between the thrust and propeller which is most suitable for the suction cup. This method rotational speed, the rotational speed and rudder angle can make the grasping system automatically adapt to are taken as the controller output signal. Simulation various objects and overcome the problems of results verify the controller is valid. overlapping and partial occlusion. SatA05 Room5 14:00-14:15 SatA05-3 IS：Data-driven adaptive control for uncertain nonlinear A New Compound Fault Diagnosis Method for Gearbox systems 13:30-15:30 Based on Convolutional Neural Network Mingxuan Xia Nanjing Univ. of Aeronautics &

Astronautics multi-source heterogeneous data, aiming to improve the Zehui Mao Nanjing Univ. of Aeronautics & intelligent operation and maintenance level of pipe Astronautics gallerys. First, interconnected distributed Rui Zhang ZhenDui Industry Artificial Intelligent heterogeneous data sources are fused into a unified data Co., Ltd. set based on the JSON-based middleware method. Bin Jiang Nanjing Univ. of Aeronautics & Second, in order to reduce the complexity of condition Jian Huang Astronautics monitoring and improve the accuracy, data with similar Muheng Wei ZhenDui Industry Artificial Intelligent characteristics are assigned to the same subspace. Co., Ltd. Then, in each subspace, the principal component analysis (PCA) method is used to mine information and This paper focus on the fault diagnosis problem for the extract features. Furthermore, the features of each compound faults of rotating machine, in which the subspace are fused, and the local outlier factor (LOF) rolling bearing and the sun gear faults simultaneously method that does not require data distribution is used to occurred are considered as the compound fault. construct the condition monitoring model and analyze Considering the traditional compound fault diagnosis the running state. Finally, the effectiveness and methods usually utilize the manual fault features superiority of the proposed method are illustrated by extraction, which are mainly dependent on engineering testing it on the operation and maintenance data of the experience, we propose a compound fault diagnosis pipe gallery and comparing it with the classical methods. method named multi-sensor based convolutional neural network (MCNN). For vibration signals of compound 14: 30-14:45 SatA05-5 faults, the different transmission paths and the positions IBLF-Based Adaptive Finite-time Neural Backstepping of the sensors means one part of the embedded single Control of An Autonomous Airship With Full State faults may have higher energy. The vibration signals Constraints collected from three sensors at different positions can Yan Wei Shanghai Jiao Tong Univ. help guarantee the completeness of the characteristics Pingfang Zhou Shanghai Jiao Tong Univ. of the compound fault. Then, the multi-sensor signals Yueying Wang Shanghai Univ. are combined together and fused by the convolutional Dengping Duan Shanghai Jiao Tong Univ. operation of the convolutional neural network (CNN) Weixiang Zhou Shanghai Jiao Tong Univ. model. The CNN model, which can automatically extract features from the vibration signals and achieve This paper investigates the finite-time attitude tracking classification, is used for fault extraction and fault control problem of an autonomous airship with recognition. The experiments are presented on the uncertainties and full state constraints. An adaptive physical platform of power transmission, and the finite-time neural backstepping control approach is proposed fault diagnosis method can be verified with the designed by using integral barrier Lyapunov functionals. satisfied performance. Radial basis function neural networks are applied to model the uncertainties. A finite-time convergence 14:15-14:30 SatA05-4 differentiator is introduced to estimate the time derivative of virtual control law. The stability analysis Multi-source Heterogeneous Data Fusion Method for shows that all the closed-loop signals of airship system Pipe Gallery Condition Monitoring are bounded, the state constraints are not violated, and Gang Wang State Grid Hebei Electric Power Co., Ltd. the convergence of attitude tracking error in small Jingwen Liu State Grid Hebei Electric Power Co., Ltd. neighborhood of the origin in a finite time can be Xiong’an New District Power Supply Co. guaranteed. Simulations are performed to verify the Guopeng Li State Grid Hebei Electric Power Co., Ltd. effectiveness of the control approach. Xiong’an New District Power Supply Co. Zhilei Li State Grid Hebei Electric Power Co., Ltd. 14:45-15:00 SatA05-6 Xiong’an New District Power Supply Co. Zhidan Gong Xiamen Great Power Geo Information Dynamic Modeling and Analysis for 6-DOF Industrial Tech. Co., Ltd. Robots Wenlin Huang Xiamen Great Power Geo Information Yingjie Li Kunming Univ. of Sci. & Tech. Tech. Co., Ltd. Jing Na Kunming Univ. of Sci. & Tech. Helan Wang Xiamen Great Power Geo Information Guanbin Gao Kunming Univ. of Sci. & Tech. Tech. Co., Ltd. Guoyuan Cai Shanghai Guyuan Electric Tech. Co., Ltd. The dynamic model of industrial robots is an important part of controllers, which affects the stability and In view of the exponential growth of the pipeline accuracy of industrial robots. In this paper, a dynamic inspection data volume, the lack of utilization and model for 6-degree-of-freedom (6-DOF) industrial robots analysis of the data, this paper proposes a method is established and analyzed. Firstly, the Modified named subspace principal component analysis (SPCA) Danevit-Hartenberg (MDH) method is used to build the for pipe gallery condition monitoring that integrates kinematic model of the industrial robot, and the 28

DDCLS’20 kinematic parameters of the robot are obtained. The information efficiency. And then, an improved kinematic model is also verified in a simulation event-driven mechanism combined with this environment. Then, with the kinematic mode, the time-efficiency window (TEW) is also designed. relationship between the force and acceleration of a Consequently, a novel event-driven distributed single link is determined using Newton’s equation and Kalman-consensus filter with time-efficiency window Euler’s equation respectively. The speed and (TEDKF) is presented. By adjusting the length of the acceleration of each link are calculated by extrapolating time-efficiency window, the effective historical data methods. According to the method of interpolation, the stored in each sensor is used to information fusion. And force and torque equations of the joints of the industrial then, the lifetime of WSNs becomes longer on the robot are acquired. The force and torque equations of condition that the similar filtering accuracy is achieved each link of the industrial robot are obtained by internal than that of the event-driven distributed iterations, and the dynamic equations of the industrial Kalman-consensus filter (EDKF). Finally, a simulation robot are finally obtained by sorting them out. In order to example is given to show the effectiveness of the verify the derived dynamic model, a dynamic simulation proposed filter. environment is constructed. The positions, velocities, SatA06 Room 6 accelerations and driving torques of industrial robot’s IS：Reinforcement learning and intelligent joints under normal working conditions are obtained by decision-making 13:30-15:30 trajectory planning. Analysis of the derived data shows Chair: Li Xia Sun Yat-Sen Univ. that the motors of each joint can meet the driving torque CO-Chair: Qianchuan Zhao Tsinghua Univ. of the robot under normal working conditions, and the positions, velocities, accelerations of the robot are able 13:30-13:50 SatA06-1 to meet the design requirement. Data-driven Computation of Natural Gas Pipeline 15: 00-15:15 SatA05-7 Network Hydraulics Gaochen Cui Tsinghua Univ. Compound Disturbance Rejection Control for Nanopositioning Using a Phase-Locking Loop Observer Qing-Shan Jia Tsinghua Univ. Wei Wei Beijing Tech. & Business Univ. Xiaohong Guan Tsinghua Univ. Pengfei Xia Beijing Tech. & Business Univ. Xi’an Jiaotong Univ. Zaiwen Liu Beijing Tech. & Business Univ. Qing Liu Beijing Gas

In nano-positioning, accuracy and speed are important Hydraulic calculation is a major path to the management issues to guarantee the system performance. Integral of natural gas pipeline network. However, the resonant control (IRC) is able to improve the bandwidth, investigated physical parameters of the pipes are and phase-locking loop observer (PLLO) based active usually obtained from blueprint or factory technical disturbance rejection control (ADRC) is capable of tests, which cause inaccurate hydraulic calculation achieving better closed-loop accuracy. By combining the result. Moreover, an initialization far from the root may advantages of PLLO based ADRC and IRC, a compound cause nonconvergence or more computing cost of control technique is proposed. The compound control Newton’s Iteration for solving the hydraulic equations. In can deal with hysteresis and vibration, which are main this paper, a data-driven hydraulic calculation method is factors affecting the accuracy and speed of a proposed, where we make two major contributions. (i) A nano-positioning stage driven by a piezoelectric parameter estimation method is provided for adjustment actuator. An identified model of a nano-positioning stage to the parameters in each pipe. (ii) An initialization is utilized, and simulations have been performed. method using neural network is proposed to make an Presented numerical results confirm the proposed estimate for the root at a first glance to ensure the compound control technology. convergence. The numerical results show that the proposed data-driven parameter estimation and 15: 15-15:30 SatA05-8 initialization are both effective. Event-Driven Distributed Kalman-Consensus Filter with Limited Memory Information 13:50-14:10 SatA06-2 Chunxi Yang Kunming Univ. of Sci. & Tech. A Fast-Convergence Method of Monte Carlo Jie Zhu Kunming Univ. of Sci. & Tech. Counterfactual Regret Minimization for Imperfect Chi Zhai Kunming Univ. of Sci. & Tech. Information Dynamic Games Xiaoyan Hu Sun Yat-Sen Univ. Consider the problem that the estimation accuracy is Li Xia Sun Yat-Sen Univ. inversely proportional to the energy consumption in Jun Yang Tsinghua Univ. process monitor with wireless sensor networks (WSNs), Qianchuan Zhao Tsinghua Univ. the concept of the time-efficiency window is proposed based on the relationship between timeliness and Among existing algorithms for solving

29 imperfect-information extensive-form games, Monte Li Xia Sun Yat-Sen Univ. Carlo Counterfactual Regret Minimization (MCCFR) and Jun Yang Tsinghua Univ. its variants are the most popular ones. However, MCCFR Qianchuan Zhao Tsinghua Univ. suffers from slow convergence due to its high variance in estimating values. In this paper, we introduce Texas Hold’em poker is a popular game worldwide and it Semi-OS, a fast-convergence method developed from attracts increasing attention from community of artificial Outcome-Sampling MCCFR (OS), the most popular intelligence as a typical decision-making problem in variant of MCCFR. Semi-OS makes two novel non-deterministic and incomplete information modifications to OS. First, Semi-OS stores all histories environment. One of the key tasks to deal with the poker and their values at each information set. Second, after game is the opponent modeling, which aims to exploit each time we update the strategy, Semi-OS requires a the opponent weakness based on history behaviors. In full game-tree traversal to update these values. These this paper, we study mixed-method opponent modeling, two modifications yield a better estimation of regrets. We one is Bayesian probabilistic model, one is the neural show that, by selecting an appropriate discount rate, network (NN)-based prediction model, and the last is Semi-OS not only significantly speeds up the opponent type identifying model. Then, we combine convergence rate in Leduc Poker but also statistically these three methods to generate an integrated agent for outperforms OS in head-to-head matches of Leduc opponent modeling. The opponents are categorized into Poker, a common testbed of imperfect information 4 types according to their risk preference of strategies. games, involving 200,000 hands. The main step in Bayesian method is calculating the posterior distribution over opponent’s strategy space 14:10-14:30 SatA06-3 and selecting the maximum probability. The main idea of Solving Imperfect Information Poker Games Using NN-based method is using observation data to improve Monte Carlo Search and POMDP Models the prediction accuracy of opponent’s hand. The main Jian Yao Sun Yat-sen Univ. idea of opponent type identifying model is building a Zeyu Zhang Sun Yat-sen Univ. classifier with two factors. Finally, we design a simplified Li Xia Sun Yat-sen Univ. poker game to conduct experiment and demonstrate the Jun Yang Tsinghua Univ. effectiveness of our methods. Qianchuan Zhao Tsinghua Univ. 14:50-15:10 SatA06-5 Recent advances achieved in the field of reinforcement Wasserstein Distance guided Adversarial Imitation learning have led AI algorithms capable of beating world Learning with Reward Shape Exploration champions in some perfect information games like Ming Zhang Tsinghua Univ. Chess and Go. However, the AI approach to imperfect Yawei Wang Tsinghua Univ. information games (such as Poker) is much more Xiaoteng Ma Sun Yat-sen Univ. difficult because the complexities in estimating hidden Li Xia Tsinghua Univ. information and behaviors of opponents may become Jun Yang Tsinghua Univ. extremely challenging. Since Markov Decision Process Zhiheng Li Tsinghua Univ. (MDP) is the underlying mathematical model of Xiu Li Tsinghua Univ. reinforcement learning with perfect information games, Partially Observable Markov Decision Process (POMDP) The generative adversarial imitation learning (GAIL) has deserves research attention for studying the games with provided an adversarial learning framework for imitating imperfect information. In this paper, we study a 16-cards expert policy from demonstrations in high-dimensional Rhode Island Hold’em poker game and present a POMDP continuous tasks. However, almost all GAIL and its model to formulate this imperfect information extensive extensions only design a kind of reward function of game. Based on the POMDP model, we use Bayesian logarithmic form in the adversarial training strategy with approach to estimate the opponent’s hand and the Jensen-Shannon (JS) divergence for all complex transform the original problem to several perfect environments. The fixed logarithmic type of reward information games. Furthermore, to handle the function may be difficult to solve all complex tasks, and challenge of explosively huge storage space and the vanishing gradients problem caused by the JS computation burdens, we develop a Monte Carlo divergence will harm the adversarial learning process. In optimization algorithm to estimate the action values of this paper, we propose a new algorithm named the POMDP model. Finally, we conduct numerical Wasserstein Distance guided Adversarial Imitation experiments in the Rhode Island Hold’em poker game to Learning (WDAIL) for promoting the performance of demonstrate the effectiveness of our approach. imitation learning (IL). There are three improvements in our method: (a) introducing the Wasserstein distance to 14:30-14:50 SatA06-4 obtain more appropriate measure in adversarial training Opponent Modeling in Poker Games process, (b) using proximal policy optimization (PPO) in Xi Yan Sun Yat-Sen Univ. the reinforcement learning stage which is much simpler to implement and makes the algorithm more efficient, 30

DDCLS’20 and (c) exploring different reward function shapes to suit of tracking differentiator (TD), extended state observers different tasks for improving the performance. The (ESO) and state error feedback (SEF) is designed. experiment results show that the learning procedure Finally, numerical simulations and experimental tests are remains remarkably stable, and achieves significant conducted to illustrate the effectiveness of the proposed performance in the complex continuous control tasks of control strategy. MuJoCo^1. 16:10-16:30 SatB01-2 15:10-15:30 SatA06-6 Design and Implementation of the PI-type Active Automatic Generation Control of Isolated Two-area Disturbance Rejection Generalized Predictive Control Microgrid with Multiple Resources Based on Learning Jia Ren Nankai Univ. Optimization Zengqiang Chen Nankai Univ. Changyou Feng National Electric Power Mingwei Sun Nankai Univ. Dispatching and Control Center Qinglin Sun Nankai Univ. Penghu Wang Hefei Univ. of Tech. Haiwei Wu State Grid Jiangsu Electric In order to overcome the limitations of the Active Power Co., Ltd. Disturbance Rejection Control (ADRC) algorithm in large Li Huang Hefei Univ. of Tech. time-delay systems, and the shortcomings of the online Hao Tang Hefei Univ. of Tech. calculation of the PI-type Generalized Predictive Control (PI-GPC) algorithm. This paper presents a PI-type Active This paper studies the automatic generation control Disturbance Rejection Generalized Predictive Control (AGC) problem of isolated two-area multiple source (PI-ADRGPC) algorithm. Simulation analysis is also microgrid energies under the influence of stochasticity carried out for linear systems, nonlinear systems and and volatility of renewable energies and loads. A Load large time-delay systems. The analysis results show that Frequency Control (LFC) model of isolated two-area the proposed algorithm has better dynamic performance microgrid with multiple distributed energy resources and stronger disturbance rejection ability than the and energy storage device is established. The wind traditional ADRC and ADRC-GPC algorithm. power, the photovoltaic output and load fluctuation are formulated as discrete-time Markov processes based on 16:30-16:50 SatB01-3 their stochastic dynamic characteristics. The Active Disturbance Rejection Control for Discrete performance of the proposed learning optimization Systems algorithm combined with the AGC principle of the power Haiyan Wang Anhui Univ. grid is demonstrated on microgrid system. At last, Jiangsu Univ. comparing SAQ-learning with traditional Q-learning and Tianhong Pan Anhui Univ. PI controller, simulation results illustrated the rationality Zhiqiang Gao Cleveland State Univ. Cleveland of the proposed microgrid model and the expected Huiyu Jin Xiamen Univ. performance of the controller. A form of active disturbance rejection control (ADRC) is SatB01 Room 1 proposed for a kind of discrete systems in this paper. ADRC technology and applications 15:50-17:50 The state space form of the system is firstly formulated. Chair: Mingwei Sun Nankai Univ. Then the extended state observers (ESO) with and CO-Chair: Shuqing Wang North China without model information are presented to estimate Electric Power Univ. states and total disturbance. The control law is formulated to reject disturbance and to track a given 15:50-16:10 SatB01-1 trajectory. As a case study, the semiconductor Active disturbance rejection control for a quadrotor UAV manufacturing process is used to validate the proposed Zhikai Wang Southwest Jiaotong Univ. solution. Comparing with the exponentially weighted Deqing Huang Southwest Jiaotong Univ. moving average controller, simulations indicate that the Tianpeng Huang Southwest Jiaotong Univ. proposed discrete ADRC is effective in cancelling Na Qin Southwest Jiaotong Univ. disturbance and in tracking the desired target.

The design of robust tracking control for a quadrotor is 16:50-17:10 SatB01-4 an important and challenging problem nowadays. In the A Comparative Study of The First Order Linear ADRC paper, an active disturbance rejection control (ADRC) and PI Controller in The Speed Control System of technique is developed for attitude and altitude tracking Permanent Magnet Synchronous Motor of a quadrotor unmanned aerial vehicle (UAV) system Yang Lei China North Vehicle Research Institute subject to external disturbances. The nonlinear Jing Xu China North Vehicle Research Institute dynamics model of quadrotor is first obtained by Yishuang Men China North Vehicle Research Institute Newton-Euler formula. Then, the control law, consisting Qiang Hao China North Vehicle Research Institute

Yue Sun China North Vehicle Research Institute (ESO) and in order to maintain system performance, a state feedback law is put forward to compensate the By analyzing the frequency domain characteristics of total disturbance. We proposed a novel design of ADRC the PI controller, a control parameter setting algorithm of for two- input-two-output (TITO) systems in this paper. ADRC controller is proposed. There are approximately The manipulated variables of the two loops are both the same frequency domain characteristics between the input into the ESO for the purpose of improving adjusted ADRC controller and PI controller. According to decoupling performance as well as the control the characteristics of frequency domain, the fair performance of ADRC controller. This design of ADRC is comparison environment of two controllers is easy to understand and has excellent decoupling ability. constructed. Finally, a speed control environment based To prove the effectiveness of proposed method, some on PMSM is built through simulation. Through the simulation examples are conducted. comparison of stability, rapidity and overshoot, the SatB02 Room 2 stability margin of ADRC and PI controller is Iterative learning control (I) 15:50-17:50 approximately the same. However, ADRC controller is Chair: Junmin Li Xidian Univ. superior to PI controller in rapidity and overshoot CO-Chair: Qiao Zhu Southwest Jiaotong Univ. performance. 15:50-16:10 SatB02-1 17:10-17:30 SatB01-5 Iterative learning control for networked nonlinear Sliding-Mode Control of Dynamic Wireless Charging EV System systems with fading communication Yayan Kang Shanghai Univ. Ganggui Qu Beijing Univ. of Chemical Tech. Yang Song Shanghai Univ. Dong Shen Renmin Univ. of China Cheng Peng Shanghai Univ. Ling Deng Shanghai Univ. In this paper, an accurate tracking problem for discrete-time nonlinear systems with output fading is The main challenge of electric vehicle (EV) dynamic investigated. It is assumed that the output signal occurs wireless technology is the fluctuation of mutual random fading during the network transmission. inductance caused by the movement of EV, which leads Random fading is mainly a multiplier disturbance to the to the instability of system. Based on the variable signal, which can be modeled by multiplying a structure control, this paper proposes the output power stochastic variable with the original signal. The modified regulation method of EV. Firstly, we use Biot-Savart Law P-type learning algorithm is proposed to solve accurate to derive the mathematical expression of mutual tracking problem. Then, the convergence of the inductance between transmitter and receiver of dynamic proposed algorithm in an almost sure sense is analyzed wireless charging (DWC) system. According to the in detail. Finally, the effectiveness of our learning mathematical expression, mutual inductance is related algorithm is verified by numerical simulation. to the lateral misalignment, longitudinal offset and vertical distance of the transmitter coil and receiver coil. 16:10-16:30 SatB02-2 Then, the state space equation based on Kirchhoff's Consensus Learning Tracking of Two-dimensional voltage / current law is established for DWC system. Discrete Networks Based on Sliding Mode Method Finally, in order to ensure the stability of the output Zijian Luo Southwestern Univ. of Finance and power, a sliding mode controller is used to adjust the Economics transmitted power for the DWC system and track the Wenjun Xiong Southwestern Univ. of Finance and reference input. Through simulation, it is proved that the Economics system output is consistent under the condition of mutual inductance fluctuation brought by the relative In this paper, the consensus tracking of two-dimensional distance change between transmitter and receiver. discrete networks is studied by designing a kind of control strategies. The designed control strategies 17:30-17:50 SatB01-6 combine the advantages of sliding mode control and A Novel Design of Active Disturbance Rejection Control iterative learning control, which can be applied in for TITO Systems general situations. And it is proven that the consensus Shuqing Wang North China Electric Power Univ. tracking can be achieved by running repeatedly in a Wen Tan North China Electric Power Univ. finite time interval. Some sufficient conditions are Donghai Li Tsinghua Univ. presented to guarantee the asymptotical convergence of Wenqing Cui North China Electric Power Univ. the tracking error. Finally, the effectiveness is illustrated by numerical simulations. As a new control method that does not need accurate information of model, active disturbance rejection 16:30-16:50 SatB02-3 control (ADRC) treats external and internal disturbance Ride Quality Improvement for High-Speed Railway as a total disturbance under extended state observer Based on D-Type Iteration Learning Control

DDCLS’20

Luping Yang Southwest Jiaotong Univ. the set-point command is proposed. The ILC law of the Qiao Zhu Southwest Jiaotong Univ. set-point is derived from the dynamical linearization of a Jun Ni Southwest Jiaotong Univ. nonlinear ideal ILC law. Then, the estimation law of the parameter in the ILC law is derived from the optimization Though differential-type (D-type) control method and of a cost function. At last, simulations demonstrate the iteration learning control (ILC) are both classic control advantages of the proposed scheme. strategy, ILC is rarely used for lateral control of trains. In this paper, a D-type ILC is proposed to suppress the 17:30-17:50 SatB02-6 lateral vibration of train body and improve ride quality. Convergence analysis of ILC process for networked First, the dynamic model of lateral and yaw motions for system with system noise train body is introduced, where external disturbances Jian Liu Xidian Univ. caused by bogies are considered. Based on the Xiaoe Ruan Xian Jiaotong Univ. repeatability of train operation, a iteration learning Yamiao Zhang Xi’an Univ. of Posts and controller in combination with D-type control method is Telecommunications employed to better suppress the lateral motions of train body. Unlike traditional PID controller, the D-type The paper is devoted to the convergence analysis of iterative learning controller can make good use of the iterative learning control process for discrete-time train’s repeatability to adjust the input force to the networked system, where the system dynamics are actuator. Then, the convergence condition is analyzed to assumed to be unknown. We use the iterative learning ensure the rationality of the proposed algorithm. In order scheme developed in [2, 12] to obtain the required to verify the effectiveness of proposed controller, the system information. By using λ-norm technique, we co-simulation of a full-scale train model with German demonstrate the effect of system noise on the learning high interference track irregularities in SIMPACK and the process for networked system. The results show that the control flow in SIMULINK is established to evaluate the tracking performance is highly dependent on the effectiveness by comparing with a open-loop system. statistical characteristics of system noise, independent of output packet loss rate and decreases as the input 16:50-17:10 SatB02-4 packet loss rate increases. In addition, a numerical Iterative learning control algorithm of consensus for example is presented to show the findings. discrete-time heterogeneous multi-agent systems with SatB03 Room 3 independent topologies Xinxin Liu Xidian Univ. Data -driven fault diagnosis and health Maintenance (II) Junmin Li Xidian Univ. 15:50-17:50 Chao He Xidian Univ. Chair: Xiaogang Deng China Univ. of Petroleum CO-Chair: Yanlin He Beijing Univ. of Chemical Tech. In this paper, leader-follower consensus problems of a kind of discrete-time heterogeneous multi-agent 15:50-16:10 SatB03-1 systems (MASs) with independent topologies are A Multi-Label Method of State Partition and Fault studied by using iterative learning control (ILC) in a Diagnosis Based on Binary Relevance Algorithm repeatable control environment. The heterogeneous Fengying Li Beijing Univ. of Chemical Tech. multi-agent systems are composed of second-order and Xin Ma Beijing Univ. of Chemical Tech. first-order dynamic systems, and independent topology Youqing Wang Beijing Univ. of Chemical Tech. refers to the topological structure of velocity and Shandong Univ. of Sci. & Tech. position is different. An iterative learning control algorithm is proposed to solve the exact consensus of Prognostics and health management (PHM) is an discrete-time heterogeneous multi-agent systems with important topic of rolling bearing, so partition of healthy independent topology. A necessary and sufficient stage and different degree of fault states are equally condition of the consensus is also given for the MASs. important as fault types. This study uses a multi-label Finally, the simulation example proves the effectiveness learning method, Binary Relevance (BR) algorithm, for of the iterative learning control algorithm. fault diagnosis of bearing data based on both of states partition and fault types judgment. The Binary Relevance 17:10-17:30 SatB02-5 algorithm simplifies the classification process by A new indirect ILC method with an iterative dynamical transforming the multi-classification problem into identification of the set-point command multiple binary classification problems. Using extreme Huaying Li Qingdao Univ. of Sci. & Tech. learning machine ensures the classification speed and Ronghu Chi Qingdao Univ. of Sci. & Tech. effect. By comparative experiments on XJTU-SY bearing datasets, the effectiveness of our method is proved to be In this work, a new indirect iterative learning control superior. (ILC) method with an iterative dynamical identification of

16:10-16:30 SatB03-2 features. Lastly, the softmax classifier is utilized to A Linear-Color Contrast Based Algorithm for Fault classify the fault patterns. To test the method Detection of Primary Spring in Train Bogie System performance, one case study on the benchmark Longkai Liu Southwest Jiaotong Univ. Tennessee Eastman industrial system is performed and Yuanjiang Hu Guangzhou Yunda Intelligent Tech. Co. the results show that MACNN gives the better fault Shupan Li Southwest Jiaotong Univ. classification results than the traditional feedforward Meng Zou Guangzhou Yunda Intelligent Tech. Co. neural network (FNN) and CNN models. Deqing Huang Southwest Jiaotong Univ. Xiaoman Liu Guangzhou Yunda Intelligent Tech. Co. 16:50-17:10 SatB03-4 Prediction of Remaining Useful Life of Proton Exchange The primary spring buffers the impact and vibration of Membrane Fuel Cell based on Wavelet-LSTM track on train. Hence, once the primary spring breaks or Yingjie Xin Univ. of Sci. & Tech. Beijing shifts, it will cause serious safety accidents. To detect Yanyan Hu Univ. of Sci. & Tech. Beijing whether the primary spring has shifted or not, the linear-color contrast (LC) algorithm is adopted to locate Remaining useful life (RUL) prediction is a promising a marked white line on the primary spring. More method for proton exchange membrane fuel cells specifically, the salient value of each pixel in the image (PEMFC) to save costs and expand applications. This is calculated to binarize the image. Then, by calculating paper proposes a model based on wavelet transform and whether the abscissa difference of the white line’s long-short-term memory (LSTM), which uses a contour between the template image and the image to be combination of neural networks and wavelet transform. detected is less than a given threshold, the primary First, the pre-processed voltage sequence of PEMFC spring in the image to be detected is determined to be data is wavelet decomposed, and LSTM prediction is shifted or not. By comparing with other threshold performed on the decomposed sub-waveforms processing techniques for image segmentation tasks, respectively, and the prediction result is wavelet including basic global threshold processing, OSTU (is reconstructed for voltage prediction. The results show also called maximum between-cluster variance (MBV)) that compared with the voltage prediction method using and Sauvola, the LC algorithm achieves the most SVM, LSTM and BiLSTM, Wavelet-LSTM can perform impressive performance in primary spring fault more accurate prediction under different load detection task. conditions.

16:30-16:50 SatB03-3 17:10-17:30 SatB03-5 Improved Convolutional Neural Network Based on Novel L2-Discriminant Locality Preserving Projection Multi-head Attention Mechanism for Industrial Process Integrated with Adaboost and Its Application to Fault Fault Classification Diagnosis Wenzhi Cui China Univ. of Petroleum Xiao Hu Beijing Univ. of Chemical Tech. Xiaogang Deng China Univ. of Petroleum Yang Zhao Beijing Univ. of Chemical Tech. Zheng Zhang China Univ. of Petroleum Yuan Xu Beijing Univ. of Chemical Tech. Yanlin He Beijing Univ. of Chemical Tech. Deep learning theory demonstrates its great power in the Qunxiong Zhu Beijing Univ. of Chemical Tech. complicated data processing field. As a prevalent deep learning technology, convolutional neural network (CNN) Nowadays, the safety of industrial production processes has achieved some successful applications in the field has been paid more and more attention. Fault diagnosis of fault detection and classification in the complicated methods based on data-driven techniques have been industrial systems because of its strong nonlinear widely discussed in recent years. For handling the high feature extraction capability. However, the basic CNN dimensional data generated by large and complex views all the features equally and cannot highlight these systems, reducing the data dimension to extract fault features which play a more important role in the fault information has been widely studied. However, the classification procedure. To deal with this problem, this performance of traditional dimension reduction methods paper proposes an improved CNN model, called is limited due to the high complexity and integration of multi-head attention CNN (MACNN), to distinguish the process data. As a result, the accuracy of fault diagnosis importance of different features for better fault is unacceptable. In order to handle this limitation, a classification performance. In the MACNN framework, novel fault diagnosis model integrating L2-Discriminant the raw training data are firstly analyzed by the multiple Locality Preserving Projection with AdaBoost is convolutional layers for feature extraction. Then, the proposed in this paper. Discriminant Locality Preserving features obtained by the convolution layers are used as Projection (DLPP) as a kind of manifold learning the input of the multi-head attention (MA) layers. The methods is good at extracting information from introduction of the MA mechanism can help exploit more high-dimensional data but its performance is subject to meaningful information by emphasizing the important the singular matrix decomposition problem. So, the L2

DDCLS’20 regularization term is incorporated into the objective the Proximal Policy Optimization (PPO), studied the function of DLPP to solve the singular matrix tracking control problem of the aircraft, and accurately decomposition problem. After extracting fault tracked the typical command signals. Fixed-point information using the proposed L2-DLPP method, simulation of the aircraft is performed, with results AdaBoost is adopted to classify faults. In order to verify showing that, in presence of aircraft model parameter the performance of the proposed fault diagnosis variation and external disturbance, the controller based methodology, a case study using the Tennessee on deep reinforcement learning can achieve accurate Eastman process (TEP) is carried out. The effectiveness tracking of overload commands. of the proposed fault diagnosis methodology is confirmed by simulation results. 16:10-16:30 SatB04-2 An Adaptive Adjustment Algorithm for Scattering Units 17:30-17:50 SatB03-6 of GNSS Ocean Reflected Signals On Highway Guardrail Segmentation Algorithms Based Bowen Li Beihang Univ. on Patrol Car Mobile Video in Complex Environments Bo Zhang Beihang Univ. Zihao Xu Chongqing Jiaotong Univ. Dongkai Yang Beihang Univ. Juan Cao Chongqing Jiaotong Univ. Di Wu Beihang Univ. Zhangli Lan Chongqing Jiaotong Univ. The Global Navigation Satellite System (GNSS) reflected In view of the fact that the amount and quality of manual signals change with the physical characteristics of the statistics work of ordinary highway guardrail is huge and reflected surface, which are different from GNSS signals. easy to be missed, the active contour algorithm based Therefore, the reflected signals are suitable in remote on the level set model, phase stretch transformation sensing field. The simulation of GNSS reflected signals algorithm, Graph Cut algorithm and Lazy Snapping is a branch of GNSS Reflectometry (GNSS-R). In most of algorithm are adopted for the purpose of the extraction the previous studies, the scattering area is solidified and and segmentation for the guardrail areas in this paper. equally spaced and the amount of calculation will These four algorithms are respectively applied to three increase. In this paper, the influence of different scene datasets collected in different environments including configurations on the size of the scattering unit is rural roads, urban roads and the national and provincial analyzed, and an adaptive adjustment algorithm for the highways environments in order to test their scattering unit is proposed by using RBF neural network effectiveness in handling environment variability. The model, which can adjust adaptively according to the experimental results show that the improved Lazy scene configuration parameters. The proposed Snapping algorithm based on Graph Cut idea can algorithm meets the requirements in accuracy and takes partition the guardrail area well in complex only 0.09 of the theoretical model time. On the basis of environments. The results provide a basis for the ensuring the simulation accuracy of the model, the subsequent research on the identification of highway calculation efficiency is greatly improved. accessory facilities based on mobile image intelligent analysis.

SatB04 Room 4 16:30-16:50 SatB04-3 IS： Data-driven smart transportation and its application 15:50-17:50 An Automated Visible / Infrared Image Analysis System Chair: Guangyue Xue China Transport of Unmanned Aerial Vehicles (UAVs) Telecommunications & Lichun Yang Beihang Univ. Information Center Dan Yang Jiangsu Automation Research Institute CO-Chair: Mingrui Hao Harbin Institute of Tech. Jianghao Wu Beihang Univ.

15:50-16:10 SatB04-1 Unmanned Aerial Vehicles (UAVs) have evolved rapidly over the past decades driven primarily by military uses. Aircraft Control Method Based on Deep Reinforcement This paper illustrates the application of UAV as a Learning platform equipped with visible/infrared sensors for Yan Zhen Harbin Institute of Tech. battlefield reconnaissance. The characteristics of UAV Mingrui Hao Harbin Institute of Tech. imagery, however, necessitates the development of an automated visible / infrared image analysis system to To address the failure of precise overload tracking and enhance the operational utility of UAVs. To achieve the anti-interference caused by the difficulty of accurate interpretation of the visible light and infrared images modeling of a complex aircraft, the controller designing acquired by the cameras mounted on UAVs, we method based on deep reinforcement learning is proposed an automatic image analysis framework in this studied. This paper trained the control network based on paper. Although this system involves many aspects of

35 data processing, for our research we chose to focus on is proposed for aviation positioning and navigation direct and indirect image georeferencing and target application by fusing BDS/GPS positioning data. tracking technique. Finally, experiments were conducted McDE-PF is used as the local filter to process the on the visible and infrared image data sets obtained from measurements from the GNSS receivers. The flight test the UAV to verify the performance of the proposed practical data is used to validate the effectiveness of system. The experimental results have demonstrated the fusion method, Comparative simulation and that the effectiveness of the proposed multispectral experiment studies confirm the validity of the presented image analysis system and the availability of UAVs as a method. reconnaissance platform. SatB05 Room 5 IS ： Advanced intelligent control method and it 16:50-17:10 SatB04-4 application 15:50-17:50 Design of High Precision Power Quality Testing Chair: Weiwei Che Qingdao Univ. Precision Calibrator CO-Chair: Xiaozheng Jin Qilu Univ. of Tech. Cheng Guo Yunnan Electrical Power Research Institute 15:50-16:05 SatB05-1 Ke Yin North China Electric Fault Detection and Classification for Wind Turbine Power Univ. Gearbox via a Time-Frequency Analysis Method Xiaohong Huang Southwest Jiaotong Univ. Caoyuan Gu Zhejiang Univ. of Tech. Junwei Zhu Zhejiang Univ. of Tech. In this paper, aiming at the periodic detection of power Dajian Huang Zhejiang Univ. of Tech. quality testing equipment, a detection scheme based on Xin Wang Heilongjiang Univ. online mode is proposed. Aiming at the high-precision problems that need to be solved in the test process, a Gearbox with complex structure is one of the most solution that comprehensively considers the fragile components of wind turbines. The fault detection measurement, filtering and sampling rate is proposed. and classification of the gearbox is crucial to reduce the Aiming at the time synchronization problem, a timing unexpected downtime and economic loss of wind method based on (Simple Network Time Protocal) SNTP turbines. The main purpose of this paper is to propose a broadcast is proposed. Finally, the voltage fluctuation to fault detection and classification method based on the measurement accuracy is analyzed. standard deviation and wavelet entropy for wind turbines. Firstly, the fault detection logic is designed by 17:10-17:30 SatB04-5 using the standard deviation and its normalized result. Secondly, the wavelet entropy is used to determine the Observer-based Optimal Adaptive Control for wind turbine gearbox fault type. The experimental Multi-motor Driving Servo System. results from historical data are consistent with the actual Shuangyi Hu Beijing Institute of Tech. maintenance report results, which verifies the Xuemei Ren Beijing Institute of Tech. effectiveness of the method. In this paper, an improved optimal sliding mode control 16:05-16:20 SatB05-2 strategy is proposed for multi-motor driving servo system.Some states of multi-motor drive system are not Data-Driven-Based Distributed Security Control for measurable and there exists unknown nonlinearity. To Vehicle-Following Platoon solve this problem, the disturbance observer and Weiwei Che Qingdao Univ. extended state observer are both applied to estimate the Chao Deng Nanyang Technological Univ. unknown states and nonlinearity. Based on optimal Dan Liu Nanyang Technological Univ. control theory, the optimal sliding surface is selected to guarantee the optimal dynamic performance of the This paper proposes a data-driven-based distributed sliding mode of the system. The effectiveness of security control approach for vehicle-following platoons designed control methods is illustrated by simulation in the presence of denial-of-service attacks. First, results. distributed resilient observers are designed to estimate the trajectories of the leader vehicle. Based on the 17:30-17:50 SatB04-6 developed observers, data-driven controllers are proposed for the vehicle-followers. Specifically, a data BDS/GPS Data Fusion Based on McDE-PF for Aircraft driven approach is introduced to identify controller Positioning and Navigation parameters without relying on the knowledge of system Guangyue Xue China Transport Telecommunications & dynamics. Finally, a vehicle-following platoon example is Information Center provided to demonstrate the effectiveness of the data-driven-based distributed security control method. A novel data fusion based on McDE-PF (particle filter integrated with memetic compact differential evolution)

DDCLS’20

16:20-16:35 SatB05-3 In this article, the state estimation issue is addressed for a class of genetic regulatory networks with discrete and Adaptive Fuzzy Control for A Class of Nonlinear leakage delays. The main purpose is to calculate and Time-delay Systems complete the system state information through the Yizhu Chen Bohai Univ. significant measurement outputs. Firstly, the original Liyuan Shao Bohai Univ. nonlinear error system is translated into a linearly Siwen Liu Bohai Univ. uncertain one by applying the Lagrange’s Mean–Value Yanlong Zhang Tieling Normal College Theorem. Secondly, a sufficient condition is established Huanqing Wang Bohai Univ. to ensure the robust asymptotic stability of error system by resorting to Lyapunov–Krasovskii functional, convex In this work, the adaptive fuzzy tracking control problem combination technique, Jensen’s inequality, linear is investigated for a class of uncertain matrix inequality combined with Barbalat’s lemma. nonstrict-feedback nonlinear systems accompanied Meantime, the state observer gains are derived in term of time-delay terms and unknown virtual control the feasible solutions to inequalities. Finally, a group of coefficients. Within this scheme, fuzzy logic systems numerical examples are given to verify the effect of (FLSs) are utilized to approach to the uncertain nonlinear leakage delay on system stability and the effectiveness functions and the backstepping technique as well as of the devised state observer. Lyapunov stability theory are introduced to design the adaptive fuzzy controller. The amount of the online 17:10-17:30 SatB05-6 adaptive parameters of the control scheme does not exceed the order of the original system, which is the Adaptive NN Finite-time Tracking Control for PMSM with advantage of the scheme. The results show that the Full State Constraints proposed adaptive fuzzy controller can ensure that all Jin-Zi Yang Liaoning Univ. of Tech. signals in the closed-loop system are bounded and the Yuan-Xin Li Liaoning Univ. of Tech. system output tracks the desired reference signals. Finally, the simulation results demonstrate the This paper proposes a finite-time adaptive tracking availability of our put forward control project. control scheme for permanent magnet synchronous motors (PMSM) with full state constraints. In order to 16:35-16:50 SatB05-4 deal with full state constraints, the nonlinear transformation function is first introduced to transform Robust Adaptive Control of A Disturbed Chua’s Circuit the constraint problem into a non-constraint problem. with Circuit Implementation Then, we will provide a tracking differentiator that get Cheng-Cheng Jiang Hefei Univ. of Tech. the differentiation of the virtual control laws. At the same Xiao-Zheng Jin Qilu Univ. of Tech. time, the neural networks (NN) are employed to approximate unknown nonlinearities. Finally, simulation In this paper, a circuit implementation scheme is results are given to validate the proposed control designed for the adaptive algorithm applied to the scheme. disturbed Chua’s circuit system. Firstly, in order to effectively suppress the adverse effects of unknown 17:30-17:50 SatB05-7 disturbances on the system, the input of the system is designed by using the method of adaptive parameter Robust Adaptive Control for DC-DC Buck Converters adjustment. Then, the asymptotic stability of the ADRC with Load Fluctuation system is proved by lyapunov theorem. Besides, the Cheng-Wei Yang HeFei Univ. of Tech. pure circuit implementation scheme of the control Xiao-Zheng Jin Qilu Univ. of Tech. algorithm is given by using the combination of some basic analog components. Finally, the software Multisim This paper mainly develops a robust adaptive tracking is used to do the circuit simulation experiments, and the control scheme to counter effects caused by load experiment results show that the circuit design scheme fluctuation and external disturbances. An adaptive of the algorithm is correct. controller based on appropriate control strategy and adaptive law is presented based on Lyapunov stability 16:50-17:10 SatB05-5 theory. The output voltage asymptotic tracking of the buck converters is obtained even under the influence of State Estimation Design for Genetic Regulatory load fluctuation and external disturbances. Simulation Networks with Discrete and Leakage Delays results also reveal the availability of the proposed Shasha Xiao Heilongjiang Univ. adaptive control scheme. Tingru Xu Heilongjiang Univ. Xian Zhang Heilongjiang Univ. SatB06 Room 6 Xiaona Yang Heilongjiang Univ. IS: Neural networks, fuzzy systems control in data driven Xin Wang Heilongjiang Univ. manner 15:50-17:50 Chair: Feng Li Jiangsu Univ. of Tech.

CO-Chair: Fei Qiao Tongji Univ. Li Wang North China Univ. of Tech. Cong Wang North China Univ. of Tech. 15:50-16:05 SatB06-1 Combined Signals Based Identification Scheme for the In order to improve the speed and robustness of Hammerstein-Wiener System with Process Noise hierarchical convolutional features for visual tracking Feng Li Jiangsu Univ. of Tech. algorithm, the multi-Gaussian correlation filters for Jia Li Shanghai Univ. visual tracking based on confidence feedback adjustment is proposed. Our method extracts features An identification scheme of the nonlinear for tracking from two appropriate convolutional layers, Hammerstein-Wiener system with colored noise with the and proposes the average feature energy ratio to reduce help of combined signals is proposed. The combined the feature dimensions, along with the sparser model signals are used to active the Hammerstein-Wiener update scheme avoiding over-fitting to improve the system, resulting in the identification process are tracking speed. Then different Gaussian labeled significantly simplified. Firstly, the correlation analysis functions are input to construct the multi-Gaussian algorithm is employed to identify the output nonlinear correlation filters and adaptively fuse all the predicted block parameters and linear block parameters, thus the results of multiple filters, expecting for higher accuracy. process noise can be compensated by correlation Finally, it introduces the average peak-to-correlation function. Furthermore, the immeasurable internal energy and the maximum response ratio to evaluate the variable and the unknown noise term in identification tracking results, and adjust the learning rate and search system are replaced with the output of designed area to further improve the robustness of the tracker. auxiliary model and estimated residual, therefore, This algorithm is tested on the OTB100 benchmark auxiliary model based recursive extended least square datasets. The results show that it has an average identification scheme can be obtained to learn the input distance precision of 88.6%, an improvement of 4.9 nonlinear block parameters and noise model parameter. percentage points over the original hierarchical Simulations results are given to demonstrate the convolutional features for visual tracking approach, as advantage of the identification scheme. well an average speed of 29.2 frames per second, 3 times that of the original method. The proposed 16:05-16:20 SatB06-2 multi-Gaussian correlation filters can effectively improve Multi-label Disease Diagnosis Based on Unbalanced the speed and accuracy and has good robustness ECG Data whatever challenges like occlusion, deformation and Peishan Rong Beijing University of Posts & similar background interferences subjecting to. Telecommunications Tao Luo Beijing University of Posts & 16:35-16:50 SatB06-4 Telecommunications A Combined Training Algorithm for RBF Neural Network Jianfeng Li Beijing University of Posts & Based on Particle Swarm Optimization and Gradient Telecommunications Descent Kai Li First Research Institute of the Ministry of Ming Xu Chinese Academy of Sci. Public Security Hao Chen Chinese Academy of Sci. Liwei Duan Fuzhou Univ. In this paper, we propose a model to predict 55 classes of heart diseases simultaneously, that is, to solve a Radial basis function neural network usually has an multi-label classification task. In order to make full use extremely complex surface of the error function for its of the characteristics of the ECG, we propose a network strong non-linear mapping capability. This brings us to structure combining residual neural network (ResNet) lot of challenges for the neural network training, and gated recurrent unit neural network (GRU). On this especially when you can’t find a suitable training basis, in order to solve the problem of imbalanced data method. The convergence rate of radial basis function set, the loss function is a improved focal loss. The neural network will be slow when the gradient descent results of experiments show the effectiveness of our algorithm is used for the training of neural network. method. More specifically, the method improves F1 Meanwhile, it also has a great possibility to fall into the score, while the hamming loss is reduced. Observing the local minimum which means the network may performs classify result of each single class, we improve F1 score badly in the real prediction. On the other hand, the and average area under the receiver operating training method based on particle swarm optimization characteristic curve (AUC) for most classes. algorithm shows the weakness on local searching ability, although it can get rid of the trouble of falling into 16:20-16:35 SatB06-3 the local minimum than the gradient descent algorithm Visual Tracking Algorithm with Multi-Gaussian to some extent. In order to solve the above problems, on Correlation Filters Based on Confidence Feedback the basis of the characteristics of the two algorithms, Adjustment this paper proposes a combined training algorithm for radial basis function neural network, which can 38

DDCLS’20 overcome the disadvantage of the above algorithm and with different scales. The results show that PPO can take advantage of the two in the meanwhile. In the always provide satisfactory solutions within a combined algorithm, the neural network is trained by the reasonable computational time. particle swarm optimization algorithm at first. As the training progresses, we replace particle swarm 17:20-17:35 SatB06-7 optimization algorithm with the gradient descent algorithm after the threshold of convergence rate is Brain Tumor Image Classification by Randomly Wired reached. Besides, some data sets in UCI are simulated Neural Networks with a Modified Method with the proposed training method. The performance of Xiaohao Du Lanzhou Univ. proposed training algorithm is validated by experimental Liangming Chen Lanzhou Univ. result and compared to other training algorithm. Zhiyi Liu Lanzhou Univ. Shuai Li Lanzhou Univ. 16:50-17:05 SatB06-5 Mei Liu Lanzhou Univ. Remaining Useful Life Prediction Based on Improved Jun Yang Jiaxing Univ. Convolutional Neural Network and Morlet Wavelet Long Jin Lanzhou Univ. Transformation Peng Zhang Tongji Univ. Brain tumors including meningioma, glioma, and Fei Qiao Tongji Univ. pituitary tumor are common tumors in the middle-aged Junxia Xing Tongji Univ. and elderly people, which are not easy to be diagnosed Junkai Wang Tongji Univ. and treated, thus effecting people’s physical health and even life. In addition to the diagnosis of brain tumors by In the production process, machine breakdown cannot doctors based on the clinical experience and be ignored since it may bring security risk and biochemical reaction results, computer-assisted production stagnation. Machine maintenance, as an diagnosis can also be relied upon, which is a product of indispensable way to avoid malfunction, may be the development of artificial intelligence. In this paper, a executed by accurately estimating the expected failure convolutional neural network with a randomly generated time of the machine. In this paper, a novel convolutional network structures is used to realize the diagnosis of neural network (CNN), based on deep learning, is brain tumor types by the magnetic resonance imaging established to predict the remaining useful life (RUL) of (MRI) images classification. The presented neural the machine. Based on the mean value interpolation network is called randomly wired neural network method and principal component analysis (PCA) and (RWNN). In addition, we propose a modified method for wavelet transformation method, data preprocessing and the RWNN model, which improves the accuracy of the feature transformation are carried out necessarily to the RWNN model in image classification by about 1% machine history operation data. Finally, bearing without increased training time. Comparison acceleration experiment data sets of 2012 PHM race are experiments include comparing the modified RWNN employed to train the prediction model of RUL of the model with the original RWNN model, the models machine. proposed by other work and some classic convolutional neural network models such as ResNet and EfficientNet 17:05-17:20 SatB06-6 for image classification. Compared with other models, A Deep Reinforcement Learning Approach to The the modified RWNN model achieves the highest brain Flexible Flowshop Scheduling Problem with Makespan tumor image classification accuracy of 95:33%. These Minimization remarkable experimental results show that, the modified Jialin Zhu Tsinghua Univ. RWNN model is an effective and feasible tool for the Huangang Wang Tsinghua Univ. diagnosis of brain tumors. In addition, it also provides a Tao Zhang Tsinghua Univ. new research direction for the neural network structure design. Recent work has demonstrated the efficiency of deep reinforcement learning (DRL) in making optimization 17:35-17:50 SatB06-8 decisions in complex systems. Compared with other DRL algorithms, the proximal policy optimization (PPO) Path-Following Control for Unmanned Rollers: A has higher stability and lower complexity. The typical Composite Disturbance Rejection-based Framework flexible flowshop scheduling problem (FFSP) with Kang Song Tianjin Univ. identical parallel machines is an NP-hard problem. This Hui Xie Tianjin Univ. paper is the first case to utilize PPO to solve the problem with makespan minimization. The particular state, action The drum roller, as a widely used engineering vehicle, and reward function are designed for the FFSP to follow has higher degree of freedom in motion relative to the Markov property. The efficiency of PPO is evaluated conventional passenger vehicles. The special operating on the wafer pickling instance and random instances condition that has large rocks on road for compaction 39 introduces severe disturbances in path-following. In this 16:10-16:30 SatB07-2 paper, a composite disturbance rejection-based framework, for the path-following control of rollers, is A Highly Efficient Joint Sparsity Constrained Robust proposed. The external disturbances caused by rocks Principal Component Analysis for Fault Diagnosis on road are rejected by correcting the coordinates of Xianchao Xiu Peking Univ. rollers from Global Position System (GPS) using Ying Yang Peking Univ. measured attitude information. The nonlinearities from Lingchen Kong Beijing Jiaotong Univ. the complex articulation structure are compensated Wanquan Liu Curtin Univ. using a kinematic model-based feedforward control. All other uncertainties, internal and external, are lumped as Principal component analysis (PCA) is one of the most an augmented state - “total disturbance”, estimated commonly used techniques in high-dimensional data hence rejected in real-time via the extended state analysis, and it has been widely applied for fault observer (ESO). As compliment to ESO with the limited diagnosis. However, the classical PCA has two performance due to low sampling rate of GPS, a model drawbacks: sensitivity to outliers and non-interpretation parameters self-learning algorithm is added. The of principal components. In this paper, a novel joint proposed solution is validated both in simulation and sparsity constrained robust principal component experiments, showing satisfactory performance. The analysis (JSCRPCA) is proposed, in which the robust maximum lateral error is ~0.1m for unmanned rollers, term makes it stable to outliers, the joint sparsity out-performing the average level of human driven rollers constraint controls row-wise sparsity, and the when working on road with maximum diameter of rocks hypergraph Laplacian considers the structure up to 1m. information. In algorithm, an efficient alternating SatB07 Multi-Function Hall direction method of multipliers is designed to solve the Best Paper Award Finalist 15:50-17:50 proposed JSCRPCA. It is proved theoretically that the generated sequence converges to a local minimizer. 15:50-16:10 SatB07-1 Based on the T2 and SPE statistics, an offline modelling and online monitoring procedure is presented. A Novel Incremental Gaussian Mixture Regression and Numerical experiments on the Tennessee-Eastman Its Application for Time-varying Multimodal Process process illustrate that JSCRPCA is able to improve the Quality Prediction detection performance significantly in terms of fault detection rate and false alarm rate. Deyang Li Zhejiang Univ. Zhihuan Song Zhejiang Univ. 16:30-16:50 SatB07-3 Data-driven soft sensor approach has been widely Enhanced Fault Detection Using Deviation Degree applied on real-time prediction and control of Penalty with Stacked Autoencoder in Industry Process difficult-to-measure quality variables. Among these approaches, the Gaussian mixture regression (GMR) Kai Wang Central South Univ. carries the potential of dealing with nonlinear and Le Zhou Zhejiang Univ. of Sci. & Tech. non-Gaussian industry problems, which has drawn Yalin Wang Central South Univ. increasing popularity and attentions in recent years. However, the fluctuation of raw materials, change of In a data-driven fault detection strategy, not only the data process environment, aging of instruments and other model, but the detection index also influences the factors will have an effect on system performances over detection performance a lot. Deep learning has a flexible time. Hence, the lack of adaptive mechanism will make and powerful ability for modeling strongly nonlinear the GMR difficult to suit for time-varying processes and processes with good generalization performance. may cause large prediction errors. In order to model However, lack of statistical interpretations, the detection time-varying industrial processes and improve the indices have to resort to nonparametric density adaptability of the conventional GMR, an adaptive soft estimation methods in most of deep models. Thus, the sensor based on incremental Gaussian mixture control limits are severely influenced by a specific set of regression (IGMR) is proposed in this paper. The training data, causing a poor robustness. incremental idea is integrated and an adaptive Simultaneously, the squashed activations in neural mechanism is added, which endow the proposed IGMR networks make the detection margin limited. To solve with the capability of adapting to new data in online these problems, a novel sample deviation degree environment. Compared to the moving window GMR penalty strategy with stacked autoencoder is proposed (MWGMR) and the just-in-time learning GMR (JITLGMR), to regularize the neurons and the reconstruction errors, the feasibility and effectiveness of the proposed IGMR which makes the neurons in each layer and the are verified both in a numerical simulation and a real-life reconstruction error of different samples tend to be industrial process experiment. converged. Thus, the robustness and the detection margin are improved. Experiment data in a multiphase

DDCLS’20 flow facility are used to verify the efficacy of the conventional reinforcement learning algorithm and the proposed algorithm. P-type iterative learning control scheme. This paper provides a new way for the application of reinforcement 16:50-17:10 SatB07-4 learning algorithm to batch process control.

Collaborative Output Tracking of Nonlinear Multi-Agent 17:30-17:50 SatB07-6 Systems via Observer-Based Learning Control Approach Xuefang Li Sun Yat-Sen Univ. Data-Driven Hydrodynamic Modeling for a Deqing Huang Southwest Jiaotong Univ. Flippers-Driven Underwater Vehicle-Manipulator System Yu Wang Chinese Academy of Sci. In this work, the collaborative output tracking control of Xuejian Bai Chinese Academy of Sci. a class of nonlinear multi-agent systems is investigated Long Cheng Chinese Academy of Sci. under the framework of adaptive learning control, where Shuo Wang Chinese Academy of Sci. a state observer is utilized to estimate the unmeasurable Min Tan Chinese Academy of Sci. system states. By updating the individual input of each subsystem simultaneously based on the collaborative In this study, a data-driven hydrodynamic modeling tracking error, the convergence of collaborative learning method for a flippers-driven underwater (co-learning) can be guaranteed. It is shown that with an vehicle-manipulator system (F-UVMS) is proposed. The appropriate unity partition on the derivative of desired modeling method combines parameter identification output, the convergence of individual learning for each technique and computational fluid dynamics simulation, agent implies the convergence of co-learning. The which makes the acquisition of hydrodynamic rigorous convergence analysis of the co-learning is coefficients efficient and convenient. The hydrodynamic provided based on the composite energy function (CEF) analysis of the biomimetic flipper propulsor is methodology. In the end, an numerical example is completed and the relationships between the generated illustrated to present the effectiveness of the proposed forces and the motion parameters of the biomimetic controller. flipper propulsor are revealed. The wake effects are analyzed to reveal the mechanism of the biomimetic 17:10-17:30 SatB07-5 flipper propulsor. Lift and drag coefficients are identified by the hydrodynamic data and the dynamic model of the Iterative Learning Control (ILC) Guided Reinforcement biomimetic flipper propulsor is derived. The proposed Learning Control (RLC) Scheme for Batch Processes hydrodynamic model is applied to the position control Xinghai Xu Xiamen Univ. strategy of the F-UVMS. The motion experiments of the Huimin Xie Xiamen Univ. F-UVMS are conducted in the sea and the motion Jia Shi Xiamen Univ. patterns of the F-UVMS are validated. Sunday, November 22, 2020 Iterative learning control (ILC) is a kind of effective learning control scheme which is mainly designed to SunA01 Room 1 solve the problems in controlling a batch or repetitive Data-driven modeling, optimization and scheduling process. Although the control performances of ILC 9:50-11:50 systems can improved from batch to batch, it still Chair: Li Jia Shanghai Univ. strongly depends on the repeatability of the process and CO-Chair: Qiang Chen Zhejiang Univ. of Tech. control target. Reinforcement learning (RL) is another learning based optimization algorithm which can be 9:50-10:10 SunA01-1 applied to many complicated decision-making scenarios. Data-driven based RL algorithms have good Calculation Method of Carbon Emission in Production robustness due to the generalization of the policy neural Process for Optimization of Polyester Low Elastic Yarn network, however, it is low-data efficiency in network Process training. In this paper, for batch process control we Ning Li Xi’an Polytechnic Univ. propose a new reinforcement learning control (RLC) Jingfeng Shao Xi’an Polytechnic Univ. scheme which is guided by classical iterative leaning control. On the one hand, this RLC scheme has Aiming at the problems of many parameters and energy capability to optimize the policy network faster than RL consumption fluctuation in the production process of algorithm without guidance, on the other hand, the polyester low elastic yarn, firstly, the mechanism of generalization of deep policy network improves the energy consumption fluctuation and coupling of various robustness of the control system. Based on the parameters was analyzed. Based on the carbon footprint numerical simulations, the effectiveness of the proposed theory, an energy consumption analysis model of control scheme is demonstrated by comparing with the polyester low elastic yarn process was constructed

41 around the production process of polyester low elastic A New Hybrid Forecasting Architecture of Wind Power yarn. Furthermore, the carbon footprint accounting was Based on A Newly Developed Temporal Convolutional carried out for the process flow, and then the carbon Networks emission and key process parameters were fitted, on the Yang Zhao Shanghai Univ. basis of the function relationship between the Li Jia Shanghai Univ. parameters, a process optimization model of polyester low elastic yarn based on the combination of Wind energy planning, dispatching and control in the signal-to-noise ratio orthogonal test and comprehensive generation and conversion of wind power generation weighting VIKOR method was constructed. Finally, industry are becoming more and more important for through the experimental verification and analysis, the today’s electrical power system, which needs accurate results show that the model reduces the carbon and stable wind speed prediction A new nonlinear hybrid emission of the production process by 4.58% compared model, SSA-TCN is proposed to improve the accuracy of with the initial state by optimizing the key process wind power prediction. Singular Spectral Analysis (SSA) parameters of polyester low elastic yarn production. On is introduced into the proposed method to decompose the premise of reducing energy consumption, the quality the original wind power sequence into four parts: trend, of polyester low elastic yarn has been improved. primary detail components, secondary detail components and noise. Moreover, a new Temporal 10:10-10:30 SunA01-2 Convolutional Networks (TCN) network is adopted in this paper instead of the current most advanced LSTM Optimal Scheduling Algorithm Based on Emergency network. The output of TCN model has a longer actual Priority for In Vitro Diagnostic Devices memory range and is better able to cope with the Jishuai Wang Suzhou Institute of Biomedical gradient disappearance. In order to verify the Engineering Technology, Chinese advancement of SSA-TCN, the experimental part shows Academy of Sci. that the performance comparison between SSA-TCN, Qing Qian Suzhou Institute of Biomedical TCN and LSTM. The result shows that SSA-TCN has the Engineering Technology, Chinese best performance of all. Academy of Sci. Qiang Zhang Suzhou Institute of Biomedical 10:50-11:10 SunA01-4 Engineering Technology, Chinese Academy of Sci. An Adaptive Unscented Kalman Filter for a Nonlinear Lei Wang Suzhou Institute of Biomedical Fractional-order System with Unknown Order Engineering Technology, Chinese Yue Miao Liaoning Univ. Academy of Sci. Zhe Gao Liaoning Univ. Wenbo Cheng Suzhou Institute of Biomedical Xiaojiao Chen Liaoning Univ. Engineering Technology, Chinese Academy of Sci. In this paper, an unscented Kalman filter (UKF) for a Xiaotian Ma Jiangnan Univ. nonlinear fractional-order system (FOS) with the unknown order is proposed. The continuous-time In Vitro Diagnosis devices (IVD) are made of hundreds of nonlinear FOS is discretized by Grunwald-Letnikov parts. At present, time-slot scheduling method makes difference, and the corresponding difference equation is some parts of the device idle when they are working. obtained. Besides, the augmented vector is presented to Therefore, this paper proposes an emergency priority establish the augmented equation with respect to the scheduling optimization algorithm. Firstly, a linear state and fractional-order, and the nonlinear functions in programming method is used to obtain the optimal the nonlinear augmented equation are performed by the solution with optimization objective of the shortest unscented transform. The state and unknown order are duration. When emergency samples arrive, rescheduling estimated by the proposed adaptive UKF to improve the is used to ensure the effectiveness of the test. Taking a accuracy of state and order estimations compare with fluorescence immunoassay as an example, the time-slot the extended Kalman filter. Finally, two examples are scheduling method and emergency priority scheduling given to verify the effectiveness. optimization algorithm are compared. The results show that he latter is 3/min faster than that of the former. When 11:10-11:30 SunA01-5 an emergency is added, the latter produces results in On the Cumulative Number of Confirmed Cases of time. The proposed method has at least 12 hours MTBF 2019-nCoV which Based on Data-Driven Concepts (Mean Time Between Failure). This proves that the Xuan Wang Anhui Univ. of Finance & Economics proposed method is more effective. Zhihui Yang Anhui Univ. of Finance & Economics 10:30-10:50 SunA01-3 On the basis of data-driven, this paper fitted two models of different states which according to the initial data of China's fight against COVID-19 epidemic. Depending on

DDCLS’20 the stage of the outbreak, we applied the two models to was presented for digital input (DI) module using a back predict the cumulative number of confirmed covid-19 propagation (BP), which was optimized by improved cases in the United States and Italy in the early stages of adaptive gravitational search algorithm (IAGSA) for the outbreak. In the early days of the outbreak, the quantitative analysis. In order to improve defect of the number of confirmed cases in the United States is basic gravitational search algorithm (GSA), a tent map expected to reach 250000 to 300,000. And if the control for population chaos initialization was proposed. Firstly, measures are effective, there is a high probability that the status information that can reflect the failure of the the number of confirmed diagnoses in Italy will not DI module was chosen as experimental data. Then, these exceed 210,000. Unity can put the difficult away. We data were handled by normalization. After that, multiple should believe that only when countries respect each IAGSA-BP models were established with status other and strengthen cooperation can they finally defeat information about DI module. The output of these the epidemic. models can fully represent the fault status of the DI module. Finally, the experimental results are shown to 11:30-11:50 SunA01-6 validate the effectiveness of the proposed approaches. Feedback Control Based Optimization of Batch 10:10-10:30 SunA02-2 Processes in the Reduced Space Bobo Kong Ningbo Institute of Tech. Research on Thermal Performance of Self-limiting Lingjian Ye Ningbo Institute of Tech. Temperature Electrical Floor Heating System Feifan Shen Ningbo Institute of Tech. Darong Huang Chongqing Jiaotong Univ. Yue Su Chongqing Jiaotong Univ. The feedback control based optimization of batch Xianyu Xia Chonqing Real Estate College processes is to enforce the necessary conditions of Qiang Jin Chongqing Jiaotong Univ. optimality in a feedback control manner, instead of explicit numerical optimizations. The control-based This paper mainly studies the surface temperature optimization does not involve parameter estimation and stability, cooling characteristics, power - surface expensive computations for optimization, while the temperature characteristics of self-limiting temperature scheme is able to cope with uncertainties and restore electric floor heating plate. And a mathematical model of optimality. However, the dimension of necessary floor radiant heating was established to explore the conditions of optimality is generally large owing to the thermal performance of Self-limiting temperature input parameterization, which significantly complicates electrical floor heating plate which applied to floor the control task. This paper proposes an approach that radiant heating systems. The results of experimental can simplify the optimization problem by identifying the tests and simulation analysis show that the self-limiting principal subspace of plant inputs, which are laid in a temperature electrical floor heating plate has a fast small dimension while still retaining most of the temperature rise response without heat storage, and its optimization performance. A batch reactor is studied to power has a good linear relationship with the surface show the effectiveness of the new approach. temperature. The heating system has stable room temperature control, which basically meets the human SunA02 Room 2 body's thermal comfort heating requirements. IS: Data-driven technologies and its applications 9:50-11:50 10:30-10:50 SunA02-3 Chair: Yi Liu Zhejiang Univ. of Tech. Unconditional Secure Topology-hiding Broadcast Via CO-Chair: Congzhi Huang North China Electric Power Shamir’s Secret Sharing Univ. Bo Mi Chongqing Jiaotong Univ. Bingqing Wu Chongqing Jiaotong Univ. 9:50-10:10 SunA02-1 Fengtian Kuang Chongqing Jiaotong Univ. Board-level Intelligent Built-in Test Design of Digital Darong Huang Chongqing Jiaotong Univ. Input Module by Improved BP Neural Network Shijie Wu Unit 78156 of the Chinese people's Yasong Wang North China Electric Power Univ. Liberation Army Congzhi Huang North China Electric Power Univ. Mengting Lin Chongqing Jiaotong Univ. Jianhua Zhang North China Electric Power Univ. Guolian Hou North China Electric Power Univ. Secure Multi-Party Computation is a hot topic in recent years. However, though Secure Multi-Party Computation The built-in test (BIT) technology plays a major role in allows each participant to calculate without making their heavy-duty gas turbine control system. The accuracy of information known to other nodes, the underlying BIT is a guarantee for the normal operation of the topology of their network is prone to exposure. Inspired equipment. In the actual production process, by Tal Moran, Bo Mi et al. designed a broadcast protocol conventional BIT has a relatively high false alarm rate. In based on NTRUE crypt, which realized topology-hiding this paper, an intelligent built-in test (BIT) design method but relying on a third party. In view of this shortcoming, 43 we proposed a topology-hiding broadcast protocol Haijiang Zhu Beijing Univ. of Chemical Tech. based on Shamir’s Secret Sharing scheme with Xuejing Wang Beijing Univ. of Chemical Tech. self-adaptive paradigm. Security and performance analyses demonstrated that our scheme is unconditional Aiming at the multi-source heterogeneous secure and computationally light-weighted. characteristics of data collected by Internet of Things wireless sensors, we investigate an IoT data 10:50-11:10 SunA02-4 classification method based on Improved Multi-Kernel Data-Driven Backstepping Control of Chemical Process Learning Support Vector Machine (IMKL-SVM). Kernel Jiawen Gao Beijing Univ. of Chemical Tech. function types and parameters are selected mainly Jingwen Huang Beijing Univ. of Chemical Tech. according to rule of thumb in traditional multi-kernel learning method. Our improved method has two steps in Modeling and control design of complex chemical determining types and parameters of the kernel function. processes are challenge tasks because of their First, the primarily types and parameters of the kernel multi-variable, time delay and non-linear features. On the function is determined by a cross-validation method. other hand, the plant dynamics are hard to characterize And then the types and parameters of the multi-kernel precisely on line when facing uncertain disturbance. In function is optimized by SVM. The experiment designed the light of this, this paper presents a data-driven two sets of data including four sensor data: temperature, backstepping control scheme for the nonlinear chemical humidity, light and atmospheric pressure. One group process. Compared with other regular chemical process data was labeled as four types: morning, mid-afternoon, control schemes, the proposed scheme is independent evening and night. And another group data was labeled of specific mathematical models, and free of decoupling as three types: day, evening and night. The classification operation, linearization, or off-line recognition and accuracy are presented using the improved method, modeling. By constructing Lyapunov function and single-kernel SVM and traditional MKL-SVM for the two feedback control rate based on real-time data, the group data. Experiment results indicate that the integral stability is guaranteed. Williams-Otto reactor IMKL-SVM method achieves higher classification example is provided to demonstrate the effectiveness accuracy for IoT data with multi-source heterogeneous and applicability of the scheme. characteristics. SunA03 Room 3 11:10-11:30 SunA02-5 IS: Higher order differential feedback control and ADRC Generative Independent Component Thermography for Improved Defect Detection of Carbon Fiber Composites 9:50-11:50 Kaixin Liu Zhejiang Univ. of Tech. Chair: Guoyuan Qi Tiangong Univ. Meili Chen Hangzhou Arcvideo Technology Co., CO-Chair: Zengqiang Chen Nankai Univ. Ltd. Zhiwen Wang Liming Vocational Univ. 9:50-10:10 SunA03-1 Yuan Yao National Tsing Hua Univ. Consensus Tracking for Discrete Distributed Parameter Jianguo Yang Zhejiang Univ. of Tech. Multi-agent Systems Via Iterative Learning Control Yi Liu Zhejiang Univ. of Tech. Cun Wang Guangxi Univ. of Sci. & Technology Xisheng Dai Guangxi Univ. of Sci. & Technology Qingnan Huang Guangxi Univ. of Sci. & Technology As one of the popular techniques for non-destructive Jingjing Wang Guangxi Sci. & Technology Normal Univ. evaluation, infrared thermography often requires the Su Wang Guangxi Univ. of Sci. & Technology assistance of data analysis models to help defect detection and identification. A novel generative In this paper, the consensus tracking problem of independent component thermography (GICT) discrete distributed parameter multi-agent systems are framework for defect detection in polymer composites is studied. The communication topology of the system proposed. It utilizes a deep convolutional generative remains unchanged, and only some agents can directly adversarial network to generate more informative obtain the trajectory information of the virtual leader. An images, which enhances the diversity of thermography iterative learning control law including the consensus data. The defect detection performance of sequential error between any two agents in the system is designed, ICT-based thermographic data analysis can be and the convergence condition of the algorithm is enhanced. The feasibility of GICT is illustrated with its obtained with the help of the contraction mapping application to the defect detection of a carbon fiber principle. In the sense of L2 norm, the consensus reinforced polymer specimen.. tracking error among all agents in the system can converge to zero along the iteration axis. Finally, 11:30-11:50 SunA02-6 simulation examples prove the applicability of the Classification Method of Indoor Environmental Data algorithm. based on Internet of Things JiaWei Fan Beijing Univ. of Chemical Tech. 10:10-10:30 SunA03-2 44

DDCLS’20

Load Frequency Control of Three-area Interconnected Xia Li Tiangong Univ. Power Systems Based on Reduced-order Active Guoyuan Qi Tiangong Univ. Disturbance Rejection Controller Xitong Guo Tiangong Univ. Yuemin Zheng Nankai Univ. Shengli Ma Tiangong Univ. Zengqiang Chen Nankai Univ. Zhaoyang Huang Nankai Univ. A high-order differential feedback control (HODFC), Mingwei Sun Nankai Univ. which is a model free control strategy, is applied to the Qinglin Sun Nankai Univ. trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) with unknown uncertainties in this paper. A reduced-order Linear Active Disturbance Rejection Two virtual control variables are introduced to decouple Control (LADRC) controller for the load frequency the quadrotor flight system. The third-order HOD control (LFC) system is studied in this paper. The power (high-order differentiator) is proposed and used to system is inevitably disturbed by various factors such estimate the differentials of reference input and the as temperature and pressure, which may cause states of system. The second-order HODFC is designed instability of the frequency and bring safety problems. to ensure the closed system has desired poles. A The ADRC controller has the characteristic of estimating controller filter is used to compensate the unknown the disturbance and then eliminating it to achieve the function and disturbance in the quadrotor system. A suppression of disturbance. Therefore, the ADRC comparison between the HODFC and active disturbance controller, theoretically speaking, can be used to solve rejection control (ADRC) is conducted. The simulation the LFC problems. We utilize the reduced-order LADRC results illustrate that the HODFC method can ensure that controller to control a three area interconnected power the quadrotor output track the desired trajectory system, and the simulation results show that the asymptotically with a smaller tracking error than the performance of the reduced-order ADRC controller is linear ADRC, even with unknown function and better than that of the traditional PID controller. Finally, disturbance. we verified the robustness of the designed controller by changing the parameters of the controlled object. 11:10-11:30 SunA03-5 On Chaos Control of Small-scale Unmanned Helicopter 10:30-10:50 SunA03-3 Based Upon HODFC On the Maximum Anti-interference Ability of Quadrotor Xitong Guo Tiangong Univ. UAV Guoyuan Qi Tiangong Univ. Xi Li Tiangong Univ. Xia Li Tiangong Univ. Guoyuan Qi Tiangong Univ. Shengli Ma Tiangong Univ. Limin Zhang Zhongyuan Univ. of Tech. The small-scale unmanned helicopter has the high main When a quadrotor UAV is interfered so much that it may rotor speed and the light body, which makes it highly deviate from the equilibrium state greatly. Due to the sensitive to disturbances. In the case of improper limitation of the output rate of the actuator, its output assembly and wind interference, the fuselage will shake control signal is always less than the control signal violently, and even produce chaotic angular velocity needed to adjust the attitude of the aircraft, which may oscillation behavior. In this paper, the situation of cause the quadrotor UAV to lose control. This paper chaotic oscillation of the angular velocity is given. The focuses on the characteristics of yaw dynamics of the high order differential feedback controller (HODFC) for six DOF underactuated quadrotor UAV, studies the the angular velocity of the helicopter is designed. The quantitative matching relationship between the design of controller is independent of the mathematical model of the control loop, the motor rate limiter and the maximum the system, with simple structure and mathematical angular velocity interference of the quadrotor UAV, significance for parameter adjustment. Finally, the through the analysis of without the actuator and with the controller is loaded after the helicopter generates actuator respectively. The maximum angular velocity oscillations in chaotic situation. The angular velocity of disturbance and the maximum attitude angular the helicopter tends to the reference value rapidly after acceleration disturbance are obtained. Finally, the great the controller is loaded, indicating that the HODFC anti-interference capability of the quadrotor UAV constructed can stabilize the chaotic oscillation of the controlled by the classical PD control method is verified helicopter’s angular velocity. by simulation. The simulation results show that the conclusion of this paper is correct. It provides a 11:30-11:50 SunA03-6 reference for engineering application. Experiment Verification of High Order Differential Feedback Control for Quadrotor UAV 10:50-11:10 SunA03-4 Shengli Ma Tiangong Univ. Trajectory Tracking of a Quadrotor UAV Based on Guoyuan Qi Tiangong Univ. High-Order Differential Feedback Control Xitong Guo Tiangong Univ. 45

Xia Li Tiangong Univ. Analyzing the collision probability of Autonomous Vehicle at crossroad An outer-loop model-free controller called high order Anqi Shangguan Xi’an Univ. of Tech. differential feedback control (HODFC) that can compute Guo Xie Xi’an Univ. of Tech. the velocity of position and yaw is applied to accomplish Dan Wang Xi’an Univ. of Tech. the flight tasks based on indoor optical positioning Rong Fei Xi’an Univ. of Tech. system. A high precision observer called high order Xinhong Hei Xi’an Univ. of Tech. differentiator (HOD) is introduced to observe the Wenjiang Ji Xi’an Univ. of Tech. differential states of the system output and given input. A filtering signal of control output is incorporated in the It is essential to assess the autonomous vehicle control law to estimate the information related to system operation safety during driving, which can avoid or model. The remarkable features of the HODFC includes reduce the collision risk by evaluating the drive safety. In model-free performance, pole assignment based on this paper, a collision risk assessment algorithm is theory, the disturbance suppression. Comparison proposed, that is quantifies the auto-drive vehicle results of the HODFC and the collision risk by the Time to Collision (TTC) frequency. proportional-integral-derivative (PID) trajectory tracking Firstly, the Long Short Time Memory network (LSTM) is experiment in height, yaw and x-y plane, and the used to predict the surrounding vehicle trajectory; disturbance experiments are presented to illustrate the Moreover, the collision point between the auto-drive superior performance of the HODFC method over the vehicle and the surrounding vehicle is determined, and PID method. the frequency distribution result of TTC is calculated by SunA04 Room 4 the Monte Carlo simulation method; Finally, the running speed& hazard probability is obtained by changing the Statistical learning and machine learning in automation running speed of auto-drive vehicle, and the running field(I) 09:50-11:50 speed & safety probability is obtained further. It can be Chair: Kuangrong Hao Donghua Univ. seen from the result that the proposed method can CO-Chair: Yan Li Shandong Univ. provide an effective evidence for decision-making layer of auto-drive vehicle, improve the running safety of 09:50-10:10 SunA04-1 vehicle, and reduce the operation risk of auto-drive Yarn-dyed Fabric Defect Detection using U-shaped vehicle. De-noising Convolutional Auto-Encoder Hongwei Zhang Xi'an Polytechnic Univ. 10:30-10:50 SunA04-3 Zhejiang Univ. Design and Application of Intelligent Diagnosis Model QuanluTan Xi'an Polytechnic Univ. Based on Data Driven Shuai Lu Beijing Univ. of Chemical Tech. Guihua Zhang Chongqing Chuanyi Software Co., Ltd. Zhiqiang Ge Zhejiang Univ. Jie Yao Chongqing Chuanyi Software Co., Ltd. De Gu Jiangnan Univ. Peng Wu Chongqing Univ.

Practical factors such as high labor cost of labelling In this paper, an intelligent diagnosis model is designed defect samples and scarcity of defect samples make it based on stress wave technology. The model includes difficult for supervised machine learning models to two parts: primary diagnosis and advanced diagnosis. solve the problem of yarn-dyed fabric defect detection. The primary diagnosis model mainly uses the K-Means To solve this problem, this paper proposes an algorithm to realize the function of automatic unsupervised yarn-dyed fabric defect detection method hierarchical warning. After the hierarchical warning is based on U-shaped de-noising convolutional triggered, the advanced diagnostic model obtains auto-encoder (UDCAE). Firstly, for tested samples of multiple sets of spectrum data through systematic yarn-dyed fabric, the training dataset was constructed sampling, and uses the enumeration algorithm to select by collecting the non-defect yarn-dyed fabric samples. the most representative one from it to achieve automatic Then, the non-defect dataset is utilized to model and output of fault information and alarm functions. The train the proposed UDCAE model. Finally, the defective intelligent diagnosis model is applied to the axial flow area can be quickly detected by calculating the residual fan. The output of the model is the same as the analysis between the original tested yarn-dyed fabric image and result of the professional, indicating that the model is its reconstructed item correspondingly. The experiment well applied to the axial flow extension. results show that the proposed method can accurately detect defects of yarn-dyed fabrics with different 10:50-11:10 SunA04-4 patterns. A Deterministic Box-covering Algorithm for Fractal Dimension Calculation of Complex Networks 10:10-10:30 SunA04-2 Fengjun Gong Shandong Univ. Yan Li Shandong Univ.

DDCLS’20

Daduan Zhao Shandong Univ. Wenling Li Beihang Univ. Lingzhi Zhang Shandong Univ. Kernel learning filters have been effective tools for Calculating the fractal dimension of complex networks addressing nonlinear function fitting problem. In these by using a box-covering algorithm has attracted filters with Gaussian kernels, the performance depends tremendous attention. However, the existing methods on the choice of the kernel width and an inappropriate have randomness characteristics and depend on heavy kernel width might degrade the learning performance. To computational bandwidth. To address these issues, further improve the learning performance, a deterministic box-covering algorithm is proposed for the fusion-based multi-kernel learning filter with maximum calculation of fractal dimension in this paper. Firstly, the correntropy criterion is proposed in this paper, in which weight of each edge is obtained by the multiplication of multiple learning filters with different kernel widths run degrees of the two connected nodes, and nodes are independently and the output estimates are fused by a colored in order of degree from large to small. set of weighting coefficients. The weighting coefficients Furthermore, the sequence of nodes with the same are treated as the posterior probabilities of the kernel degree is rearranged to get the minimum number of widths in effective and they are computed recursively by boxes. The lastly, the fractal dimensions of a theoretical using the likelihood functions. Simulation results show scale-free network and three real networks are that the proposed filter outperforms the existing single investigated by the deterministic box-covering kernel learning filters and the multi-kernel learning filter algorithm. All these results demonstrate that with maximum mixture correntropy criterion. deterministic box-covering algorithm is serviceable in SunA05 Room 5 fractal dimension calculation of complex networks with IS：Iterative learning control and it's applications high accuracy and less calculation. 09:50-11:50 11:10-11:30 SunA04-5 Chair: Haoping Wang Nanjing Univ. of Sci. & Tech. A Parallel Feature Extraction Model with Channel CO-Chair: Hongtao Ye Guangxi Univ. of Sci. & Tech. Attention for Button Defect Detection Guilin Univ. of Electronic Tech. Jiahao Wang Donghua Univ. Kuangrong Hao Donghua Univ. 09:50-10:07 SunA05-1 Bing Wei Donghua Univ. Consensus tracking for discrete distributed parameter Lei Chen Donghua Univ. multi-agent systems via iterative learning control Xuesong Tang Donghua Univ. Cun Wang Guangxi Univ. of Sci. & Tech. Guangxi Sci. & Tech. Normal Univ. Defect detection is a vital task for production process, Xisheng Dai Guangxi Univ. of Sci. & Tech. which greatly affects the quality of the product. Manual Qingnan Huang Guangxi Univ. of Sci. & Tech. inspection is the most commonly used defect detection Jingjing Wang Guangxi Sci. & Tech. Normal Univ. method, but is very time-consuming and not reliable. Su Wang Guangxi Univ. of Sci. & Tech. Currently, many image processing algorithms have been used for defect detection to partially replace manual In this paper, the consensus tracking problem of detection. However, due to the complexity of various discrete distributed parameter multi-agent systems are defects, how to correctly and quickly detect whether the studied. The communication topology of the system button is defective still faces great challenges. To remains unchanged, and only some agents can directly address these challenges, we propose a button defect obtain the trajectory information of the virtual leader. An detection model based on parallel feature extraction iterative learning control law including the consensus network. The model extracts feature by two parallel CNN error between any two agents in the system is designed, structures, one of which is deeper and stronger (CNN-B), and the convergence condition of the algorithm is and the other is relatively shallow and weak (CNN-A). obtained with the help of the contraction mapping The CNN-B is trained on ImageNet firstly. Meanwhile, principle. In the sense of L2 norm, the consensus only the three layers of CNN-B but all layers in CNN-A tracking error among all agents in the system can are trained. We further add channel attention module to converge to zero along the iteration axis. Finally, perform feature maps recalibration in CNN-A. The simulation examples prove the applicability of the experimental results show that the parallel feature algorithm. extraction model can identify the defect of button effectively. 10:07-10:24 SunA05-2 Bipartite consensus of second-order multi-agent 11:30-11:50 SunA04-6 systems with multiple input delays Fusion-based multi-kernel learning filter with maximum Zhongqiu Chen Guangxi Univ. of Sci. & Tech. correntropy criterion Hongtao Ye Guangxi Univ. of Sci. & Tech. Lin Chu Beihang Univ. Guilin Univ. of Electronic Tech.

Junzheng Guo Guangxi Univ. of Sci. & Tech. IoTs and robots. It eliminates third-party interference and Wenguang Luo Guilin Univ. of Electronic Tech. data in the blockchain are stored in an encrypted way Kene Li Guangxi Univ. of Sci. & Tech. permanently and anti-destroys. In this paper, a methodology of blockchain is proposed and designed This study deals with the bipartite consensus problem of for advanced cooperative system with artificial second-order multi-agent systems (MASs) with multiple intelligence to protect privacy and sensitive data input delays. The main mission is to seek out the exchange between multi-agents. The validation maximum permissible time delay of MASs under the procedure is performed in laboratory by a three-level condition that each agent has different input delay. computing networks of Raspberry Pi 3B+, NVIDIA Jetson Using mathematical analysis methods, the maximum Tx2 and local computing server for a robot system with allowable delay of the system is obtained through four manipulators and four binocular cameras in peer calculation, which is determined by the maximum computing nodes by Go language. eigenvalue of Laplace matrix and control parameters. Simulation shows the validity of the proposed control 10:58-11:15 SunA05-5 protocols. Joint Configuration Optimization of Manipulator at Faulty Time Based on Manipulability Maximization 10:24-10:41 SunA05-3 Bei Liu Guangxi Univ. of Sci. & Tech. Adaptive Iterative Learning Vibration Control of a Kene Li Guangxi Univ. of Sci. & Tech. Two-Link Rigid-Flexible Manipulator with Endpoint Input Zeng Zhang Guangxi Univ. of Sci. & Tech. Saturation Qiaoliang Mo Guangxi Univ. of Sci. & Tech. Xingyu Zhou Nanjing Univ. of Sci. & Tech. Xisheng Dai Guangxi Univ. of Sci. & Tech. Haoping Wang Nanjing Univ. of Sci. & Tech. Hongtao Ye Guangxi Univ. of Sci. & Tech. Yang Tian Nanjing Univ. of Sci. & Tech. Xisheng Dai Guangxi Univ. of Sci. & Tech. When a redundant manipulator suffers a joint failure, the flexibility of the manipulator operation will be affected, An adaptive iterative learning vibration control (AILVC) which will result in the failure of the task execution. In scheme is considered for a two-link rigid-flexible order to make the manipulator have more fault-tolerant manipulator with endpoint input saturation. A PD-type performance, joint configuration optimization of AILVC law is designed for the coupled ordinary manipulator at faulty time based on manipulability differential equation-partial differential equation dynamic maximization is proposed. In this paper, the model in the presence of time-varying disturbances and manipulability is used as the fault-tolerant performance the distributed disturbance. Then, by utilizing the index of the manipulator. By constructing the equation composite energy function approach and rigorous between manipulability and joint angles, the optimal analysis, the tracking error is asymptotically guaranteed joint angle is finally obtained at faulty time, making the convergence to the angular position, and the vibration of manipulator manipulability to be maximized. the flexible link is suppressed simultaneously. Finally, the results of numerical simulations are given to 11:15-11:32 SunA05-6 demonstrate the validity of the proposed AlLVC method. Open-Closed-Loop Iterative Learning Control for Discrete-Time Systems with Vector Relative Degree 10:41-10:58 SunA05-4 under Securing core information sharing and exchange by Yun-Shan Wei Guangzhou Univ. blockchain for cooperative system Zhi-Bin Weng Guangzhou Univ. Lei Ding Wuhan Textile Univ. Teng-Fei Xiao Sun Yat-sen Univ. Shida Wang Wuhan Textile Univ. Renzhuo Wan Wuhan Textile Univ. An open-closed-loop iterative learning control (ILC) Guopeng Zhou Wuhan Textile Univ. scheme is presented for linear discrete-time Hubei Univ. of Sci. & Tech. multiple-input multiple-output (MIMO) systems with iterative varying duration in this article, where the vector The privacy protection and information security are two relative degree of MIMO system is considered. To crucial issues for future advanced artificial intelligence recompense the absent tracking information at former devices, especially for cooperative system with rich core iterations caused by iterative varying duration, the data exchange which may offer opportunities for feedback control strategy is adopted in modified attackers to fake interaction messages. To combat such tracking error based ILC law. It is proved that the threat, great efforts have been made by introducing trust convergent condition depends on the P-type control mechanism in initiative or passive way. Furthermore, gain. By selecting the proper feedback control gain, the blockchain and distributed ledger technology provide a developed open-closed-loop ILC scheme can expedite decentralized and peer-to-peer network, which has great the convergent speed. Finally, a numerical example is potential application for multi-agent system, such as provided for validation.

DDCLS’20

11:32-11:50 SunA05-7 Data driven Adaptive Cooperative Control for Urban ILC for a class of linear parabolic DPSs with moving Traffic Signal Timing in Multi-intersections boundaries Honghai Ji North China Univ. of Tech. Hai Zhang Guangxi Univ. of Sci. & Hao Liu North China Univ. of Tech. Tech. Shida Liu North China Univ. of Tech. Xisheng Dai Guangxi Univ. of Sci. & Li Wang North China Univ. of Tech. Tech. Lingling Fan Beijing Information Sci. & Tech. Univ. Muhammad Shamrooz Aslam Guangxi Univ. of Sci. & Tech. This paper presents a new distributed data-driven Su Wang Guangxi Univ. of Sci. & adaptive cooperative control method (DDACC) for urban Tech. traffic signal timing which can achieve the multi-directional queuing length balance with This article focuses on the iterative learning control changeable cycle in multi-intersections. This method problem of a class of linear parabolic distributed can guarantee the consensus convergence of the parameter systems, which has the characteristic that the distributed coordinated errors of queuing length with the boundaries of the spatial domain change continuously goal of reducing traffic congestion in multi-agent traffic with time. Then, the open loop P-type iterative learning systems. The proposed DDACC has three novel method is used to study the system output tracking features, merely using the collected I/O traffic queueing problem. Through rigorous theoretical analysis, some length data and network topology of multi-directional methods such as the contraction mapping approach and signal controllers at multi-intersections, considering Bellman-Gronwall inequality are used to prove the maximum and minimum green time constraints, well convergence of the tracking error. Finally, the working on both undersaturation and supersaturation effectiveness of the algorithm is verified by numerical traffic flow conditions. The results are illustrated by simulation. numerical and experimental comparison simulations SunA06 Room6 which are performed on a VISSIM-VB-MATLAB joint IS：Data-driven fault diagnosis, intelligent control and simulation platform. public security of network traffic 09:50-11:50 Chair: Honghai Ji North China Univ. of Tech. 1 0 : 30-10:50 SunA06-3 CO-Chair: Shida Liu North China Univ. of Tech. Data -driven Distributed Consensus Filter for a Discrete-time Nonlinear Sensor Network 09:50-10:10 SunA06-1 Chuandong Bai North China Univ. of Tech. Data-driven adaptive filtering for fault diagnosis of a Honghai Ji North China Univ. of Tech. class of nonlinear discrete-time systems Yuzhou Wei North China Univ. of Tech. Hongjiang Ji China Shipbuilding IT Co., Ltd. Zhonghua Pang North China Univ. of Tech. Lingling Fan Beijing Information Sci. & Tech. Univ. Zhongsheng Hou Qingdao Univ. Xindong Gui Beijing Information Sci. & Tech. Univ. Chenkun Yin Beijing Jiaotong Univ. In this work, a novel data-driven distributed consensus Rongmin Cao Beijing Information Sci. & Tech. Univ. filter (DD-DCF) is proposed based on the dynamic linearization technique (DLT) for a discrete-time Data-driven adaptive filtering technique has been widely nonlinear sensor network. Compared with conventional concerned due to its extensively used in industry control model-based consensus filters, the proposed method is system. In order to handle actuator fault diagnosis data-driven merely depending on the input and output problem with a class of nonlinear discrete-time systems, (I/O) data from measurements. Both the data-driven this paper investigates a novel data driven adaptive system identification (DD-SI) algorithm and the filtering based on dynamic linearization technique in the distributed consensus filter state estimation (DCF-SE) framework of Kalman Filter and Recursive Least algorithm are investigated for a nonlinear sensor Square(RLS) algorithm. Compared with the conventional network. The theoretical analysis shows the main result fault diagnosis algorithm, by modeling actuator faults of the DD-DCF algorithm in the sensor network. The changes, the proposed data-driven adaptive filtering for simulation results verify the effectiveness of the fault diagnosis (DDAF-FD) method is performed through designed approach. joint state-parameter estimation and designed by only using measurement I/O data without precise 10:50-11:10 SunA06-4 mathematical model or linearization approximation. Dynamical Optimization of Area Offset Based on Arterial Numerical examples are presented to verify the Coordination effectiveness of the proposed algorithm. Zhonghe He North China Univ. of Tech. Hongjia Wang North China Univ. of Tech. 10:10-10:30 SunA06-2 49

Li Wang North China Univ. of Tech. functionality of routing between topology concealment is never a trivial task in consideration of efficiency, In view of the complexity of traditional area offset robustness, and scalability. Though a certain of optimization model and large amount of calculation, it is topological-hiding protocols have emerged in recent difficult to realize real-time offset optimization. This years, they are faulty with third-party dependence and paper firstly establishes the network traffic flow model high latency. This paper focuses on devising a broadcast based on the Cell Transmission Model (CTM), and then protocol exploiting the structure of ideal. We also obtains the functional relationship between vehicle implemented it utilizing the Chinese Remainder Theorem arriving rate and offset, so as to realize the rolling (CRT) and compared it with previous schemes. It is optimization of offset. Secondly, this paper redefines the proved that our protocol is unconditionally secure under arterial in the road network, and optimizes each arterial the game-based definition of topology hiding and according to the priority index from high to low, so as to capable of broadcasting the information with few realize the optimization of offset in the whole road iterations. Besides, the simulation also demonstrates its network. Finally, for a road network containing 11 preferable computation/communication overhead in intersections, Vissim tool is used for simulation contrast with existing protocols, not to mention that any analysis. The simulation results prove that the proposed third-party would be unnecessary. offset optimization method has a good control effect for SunB01 Room1 the large traffic flow in the network. Iterative learning control (II) 13:30-15:30 Chair: Miao Yu Zhejiang Univ. 11:10-11:30 SunA06-5 CO-Chair: Qiuzhen Yan Zhejiang Univ. of Water Optimal Control of Vehicle Formations with Considering Resources and Electric Power Queueing Dynamics of Signalized Intersections Zhonghe He North China Univ. of Tech. 13:30-13:50 SunB01-1 Quan Zhang North China Univ. of Tech. Li Wang North China Univ. of Tech. Robust Adaptive Repetitive Control of Robotic Manipulators With the rapid development of economy, traffic Sheng Zhu Zhejiang Univ. City College congestion has become an important cause of urban Mingxuan Sun Zhejiang Univ. of Tech. traffic delays and waste of transportation resources. V2X communication technology is an important part of the This paper presents a robust adaptive repetitive control development of intelligent transportation in the future, (RARC) method for trajectory tracking of uncertain which enables vehicles to communicate with other robotic manipulators. Repetitive control is applied for vehicles, other road users and road infrastructure. The periodic trajectory tracking and a σ modification is current research works typically design the formation introduced in the periodic learning laws to guarantee the strategies based on the signal state, with no robustness of the system. All the signals in the closed consideration of queue profile in the intersection. Thus, loop are proved to be bounded. An open-loop learning by combining the characteristics of traffic flows and V2X algorithm with switching σ modification is designed to vehicles, this paper designs the formation strategies, achieve asymptotic convergence of the tracking errors comprehensively considering queueing dynamics and when the disturbances disappear. The simulation is signal states of signalized intersections. Determina-tion made to show the effectiveness of the algorithms. of V2X formation vehicles according to the division of driving state, the trajectory planning under different 13:50-14:10 SunB01-2 conditions and the formation model are established to Formation tracking for discrete-time multi-agent system maximize the utilization efficiency of the intersection. with unknown dynamics by using adaptive finite rational Finally, the effectiveness of the model is verified by orthogonal basis functions experimental simulations. Yu Ge Shanghai Univ. Yong Fang Shanghai Univ. 11:30-11:50 SunA06-6 Zhichao Sheng Shanghai Univ. Self-adaptive Topology-hiding Broadcast based on CRT Bo Mi Chongqing Jiaotong Univ. This paper considers iterative learning control (ILC) for Tiancheng Wei Chongqing Jiaotong Univ. formation tracking problem of discrete-time multi-agent Darong Huang Chongqing Jiaotong Univ. system with unknown system dynamics. It is assumed Yang Li Chongqing Jiaotong Univ. that each agent has its own system dynamics. Thus, the unknown system dynamics and information interaction In the course of network communication, the exposure between multi-agent make formation tracking difficult to of network topology may seriously result in leaking achieve. In this paper, we utilize the adaptive Fourier sensitive information to attackers, saying social decomposition (AFD) algorithm to approximate the relationships or financial flows. Howbeit, balancing the system parameters only with input and output data. Then 50

DDCLS’20 a learning control scheme is proposed with estimated Adaptive Learning Control for Nonparametric Systems system dynamics. Simulations show trajectories of with Bouc-Wen Hysteresis Input multi-agent are tracked precisely after a certain number Weigang Huang Zhejiang Univ. of Water of iterations. Resources & Electric Power Leiyu Cheng Zhejiang Univ. of Water 14:10-14:30 SunB01-3 Resources & Electric Power Qiuzhen Yan Zhejiang Univ. of Water Iteration-dependent High-order Internal Model based Resources & Electric Power Iterative Learning Control for Continuous-time Nonlinear Jianping Cai Zhejiang Univ. of Water Systems Resources & Electric Power Miao Yu Zhejiang Univ. Xiaohui Guan Zhejiang Univ. of Water Sheng Chai Zhejiang Univ. Resources & Electric Power

In this paper, an adaptive iterative learning control In this paper, an adaptive iterative learning control (AILC) scheme based on high-order internal model scheme is proposed for a class of nonparametric (HOIM) is presented for a class of nonlinear systems with hysteresis input described by Bouc-Wen continuous-time systems with unknown time-iteration- model. First, based on analyzing the property of varying parameter. The time-iteration-varying parameter Bouc-Wen model, the adaptive learning controller is is generated by a general iteration-dependent HOIM with designed by using Lyapunov synthesis. In the control iteration-varying order and coefficients. Compared with design, the nonparametric uncertainty and hysteresis the existing works based on iteration-invariant HOIM nonlinearity is compensated by robust strategy and with fixed order and coefficients, our work significantly iterative learning strategy together, according to the expands the application scope of HOIM-based ILC. Using property of Bouc-Wen model. As the iteration increases, the designed HOIM-based iterative learning controller, the system state can track its reference signal accurately the learning convergence along the iteration axis is over the whole period. Numerical results demonstrate guaranteed through rigorous theoretical analysis under the effectiveness of the adaptive learning control Lyapunov theory. Furthermore, the effectiveness of the scheme. proposed method is demonstrated according to the simulation results. 15:10-15:30 SunB01-6 Spatial Adaptive Iterative Learning Control for Automatic 14:30-14:50 SunB01-4 Driving of High Speed Train Iterative Learning Control of the Non-repetitive Zhenxuan Li Beijing Institute of Petrochemical Tech. Continuous-time System Zhongsheng Hou Qingdao Univ. Tingting Zhao Guangxi Univ. of Sci. Chenkun Yin Beijing Jiaotong Univ. & Tech. Xisheng Dai Guangxi Univ. of Sci. This paper develops spatial adaptive iterative learning & Tech. control (SAILC) method for the Automatic Train Su Wang Guangxi Univ. of Sci. Operation (ATO) of high speed train in order to drive the & Tech. train speed to follow the desired displacement-speed Muhammad Shamorooz Aslam Guangxi Univ. of Sci. trajectory. By utilizing spatial state differentiator, the & Tech. temporal nonlinear train control system is transformed to a spatial nonlinear train control system. The system This paper considers the convergence for iterative uncertainties which are space dependent are learned by learning control (ILC) of the non-repetitive a spatial parametric updating law. By adopting the continuous-time system. The non-repetitive system is spatial composite energy function, the convergence of characterized by iteration-varying uncertainties in the the SAILC for train control system is obtained. plant model matrices, the initial state, disturbances and Furthermore, the convergence of the speed error in the desired output during a finite time. We employed the spatial domain under iteration-varying initial speed of P-type learning law to modify the control input, where the proposed SAILC is made. Numerical results of the the learning gain matrix varies with iteration number, train operation are given to verify the effectiveness of then used λ-norm and Gronwall-Bellman’s Lemma to the proposed SAILC method. prove iteration-varying state and control input are SunB02 Room 2 bounded. Moreover the tracking error can converge to Applications of data-driven methods to industrial zero when the iteration-varying uncertainties all processes 13:30-15:30 converge with increasing iteration. Numerical simulation Chair: Cunwu Han North China Univ. of Tech. illustrates the effectiveness of ILC scheme. CO-Chair: Zhiqiang Ge Zhejiang Univ. 14:50-15:10 SunB01-5

13:30-13:50 SunB02-1 local connection, weight sharing, pooling and multi-layer structure, which can effectively extract local features of An Analytical Discretization Approach to Continuous- the water wall to build the intelligence defect detection time System for Kalman Filter model. Finally, the proposed method is used in the Lijun Zhang China Xi’an Satellite Control Center defect detection of the water wall of an actual thermal State Key Lab. of Astronautic Dynamics power plant. The experimental result has proved the Jian Xu China Xi’an Satellite Control Center effectiveness of the proposed method, and the recall Yonghua Li State Key Lab. of Astronautic Dynamics rate and identification speed of the proposed method Ruoyan Zhao China Xi’an Satellite Control Center can meet the requirements of intelligent detection of the Shenggang Wu State Key Lab. of Astronautic Dynamics water wall defects. Mengfan Pan Southwest University of Sci. & Tech. Lei Lei China Xi’an Satellite Control Center 14:10-14:30 SunB02-3 Jianping Liu China Xi’an Satellite Control Center State Key Lab. of Astronautic Dynamics Root Cause Diagnosis of Plant Wide Oscillations using Yi Lu China Xi’an Satellite Control Center Kernel Granger Causality State Key Lab. of Astronautic Dynamics Longfei Deng Shanghai Univ. Jianguo Wang Shanghai Univ. This paper presents an analytical discretization Jingru Su Shanghai Univ. approach for continuous-time systems. By using the Yuan Yao National Tsing Hua Univ. matrix multiplying expression of the state transition Jianlong Liu Shanghai Minghua Electric Power Sci. matrix and the matrix quadratic form theory, the general &Tech. Co., Ltd. discretized model of the system state equation in the time domain is derived. The design of the parameter N is Plant wide oscillations are very common in industrial used to ensure the accuracy and robustness of the processes. When a control unit oscillates in the process, algorithm. Compared with the traditional methods in the oscillation will propagate through the connectivity deducing the discretization formulas, the proposed between units, which will cause problems such as poor method has the advantages of generality and easiness. product quality and higher energy consumption. This method is propitious to the integrated realization of Therefore, it is very important to diagnose the root cause the Kalman filter process for the continuous-time of plant wide oscillations [1]. In this dissertation, the system. Simulation results verify the validity and kernel Granger causality is proposed for root cause feasibility of the proposed method. diagnosis, which effectively solves the problem that the linear Granger causality cannot deal with nonlinear data. 13:50-14:10 SunB02-2 A small nonlinear numerical example shows the validity of kernel Granger causality. In the root cause diagnosis Intelligence Defect Detection of the Water Wall in Boiler of industrial cases, the method also successfully System Based on CNN detected the correct root point (LC2). Yuwei Wang Janbi Power Plant of National Energy Group 14:30-14:50 SunB02-4 Li Lu Suzhou Nuclear Power Research Institute Co., Ltd. A Spatial-information-based Semi-supervised Soft Yu Zhang Janbi Power Plant of National Sensor for f-CaO Content Prediction in Cement Industry Energy Group Xiaoyu Jiang Zhejiang Univ. Yongsan Ding Janbi Power Plant of National Le Yao Zhejiang Univ. Energy Group Gaopan Huang Alibaba Group, Ali Center. Jia Yang Suzhou Nuclear Power Research Jinchuan Qian Zhejiang Univ. Institute Co., Ltd. Bingbing Shen Zhejiang Univ. Yishi Lv Suzhou Nuclear Power Research Lu Xu Shandong Donghua Cement Co., Ltd. Institute Co., Ltd. Zhiqiang Ge Zhejiang Univ. Bo Ma Beijing Univ. of Chemical Tech. Xiaoyong Lin Beijing Univ. of Chemical Tech. f-CaO is a key factor affecting the quality of cement in production. In this paper, the cement clink production As an important part to ensure the normal operation of process is introduced and discussed in detail. The time the boiler, the defect detection of water walls based on delay between the variables leads to an inaccurate the internal surface information of water walls has been matching relationship with each other and defects the paid more and more attention. But the traditional manual performance of traditional soft sensors. To this end, a detection method is time-consuming and laborious with semi-supervised spatial-information-based soft sensor the low efficiency. Therefore, an intelligence detection for f-CaO content is proposed. First, we analyzed the method for detecting water wall defects in boiler relationship between process variables and quality systems based on the convolutional neural network variable and then reconstruct the input of samples into (CNN) is proposed. The CNN has the characteristics of data matrix by stitching unlabeled process data together.

DDCLS’20

The semi-supervised structure helps retain process improve the inference accuracy. Finally, for process information in the data. Then, an end-to-end soft sensor monitoring, a fault detection panel is designed in the based on CNN is established: convolution and pooling latent and residual domains generated by the deep operations are used to extract the features of model. Through the simulation of the industrial two-dimensional data containing spatial information; a benchmark Tennessee Eastman Process, the superiority multi-layer perceptron models the extracted features of the proposed method is evaluated and discussed. regressively. Further, in order to solve the defect of SunB03 Room 3 insufficient generalization ability of the CNN-based Data-driven fault diagnosis and health maintenance (III) model, a framework for spatial feature extracting and 13:30-15:30 transferring is proposed. Compared with the multilayer Chair: Tianzhen Wang Shanghai Maritime Univ. perceptron, strong regression models with spatial CO-Chair: Dazi Li Beijing Univ. of Chemical Tech. features get better prediction accuracy. An actual cement production case is used to verify the 13:30-13:50 SunB03-1 effectiveness of the proposed method. An Imbalance Fault Detection Approach based on 14:50-15:10 SunB02-5 Differential Concordia Transform for Marine Current Turbine On Simulation Control System of Automatic Production Tao Xie Shanghai Maritime Univ. Line Based On PLC Data Drive Tianzhen Wang Shanghai Maritime Univ. Xiaojia Yang North China Univ. of Tech. Cunwu Han North China Univ. of Tech. Marine current turbine (MCT) has gradually entered and Lei Liu North China Univ. of Tech. contributed to world energy resources. However, MCT Xiaoping Zhang North China Univ. of Tech. imbalance fault often occurs due to the blades are attached by marine biological growth or marine With the development of science and technology and the pollutants, and this imbalance fault will disorder the expansion of intelligent manufacturing technology, there stator current or output power of generator. In this paper, are more and more automatic production lines in China, a novel method based on Differential Concordia but the overall structure of the automatic production line transform (DCT) for MCT imbalance fault detection is is complex and the R & D investment cycle is long, presented. The goal of this method is to provide a resulting in low production efficiency. From the point of sensitive and robust fault indicator to detect the view of automatic production line structure and data imbalance fault of MCT under wave and turbulence. The virtual simulation, this paper studies the simulation proposed method acquires the 3-phases stator current control debugging system of automatic production line from the generator and using Concordia transform (CT). driven by PLC data. According to PLC data, the Then, reconstruct the Concordia transform components developer can carry out virtual commissioning of (CTC) to gain the Concordia transform modulus (CTM) automatic production line and verify the timing of and calculate differential to remove trend. Finally, the production line. frequency spectral analysis is used to monitor the condition of blade. A 230-W prototype experimental 15:10-15:30 SunB02-6 study verified that the proposed method provides an Industrial Process Modeling and Fault Detection with effective fault indicator for MCT imbalance fault. Recurrent Kalman Variational Autoencoder Zheng Zhang Nanyang Tech. Univ. 13:50-14:10 SunB03-2 Jinlin Zhu Nanyang Tech. Univ. State of Charge Estimation for Lithium-ion Batteries Yang Liu Nanyang Tech. Univ. Based on Adaptive Fractional Extended Kalman Filter Zhiqiang Ge Zhejiang Univ. Shizhong Li Shandong Univ. Yan Li Shandong Univ. This article proposes the recurrent Kalman variational autoencoder, an end-to-end trainable framework for Yue Sun Shandong Univ. process modeling and fault detection. Based on the Daduan Zhao Shandong Univ. backbone of variational autoencoder, our research Chenghui Zhang Shandong Univ. focuses on the dynamic and nonlinear properties that implied in the industrial process. For the dynamics Battery state of charge (SOC) estimation is crucial for describing, the Kalman filter is integrated to estimate the battery management systems to ensure the reliability hidden state uncertainty due to process noise, and and safety of electric vehicles. To achieve accurate SOC hence to identify the time-domain correlation in the estimation, the fractional-order model which can hidden space. While for the nonlinear evolution in the accurately describe the diffusion and polarization of hidden mapping, the dynamic parameter network is batteries is established and parameterized by particle constructed for recurrent updating, so as to further swarm optimization firstly. Then, the Kalman filter

53 method that can realize optimal estimation of systems is of different states are input into PNN to realize fault combined with fractional calculus by utilizing fractional diagnosis. The experimental results show that this state function. Consequently, the fractional extended method can diagnose bearing fault quickly and Kalman filter (FEKF) is built up, in which an adaptive effectively, and it can be applied to wind power fault variance update algorithm is adopted to improve the diagnosis. convergence speed and robustness. Finally, the proposed algorithms are applied to two dynamic 14:50-15:10 SunB03-5 working conditions and the experimental results indicate Using semi-supervised cluster method to correct the that the adaptive FEKF is efficient and accurate in SOC mislabeled training samples of ECG signals estimation. Pengfei Wu South China Univ. of Tech. Senping Tian South China Univ. of Tech. 14:10-14:30 SunB03-3 Comparing the Effectiveness of Two Convolutional The classification accuracy of electrocardiogram (ECG) Neural Networks Methods on Fault Diagnosis signals will decrease when the labels of some samples Yinping Liu Xidian Univ. in the training set are incorrect. To mitigate this negative Junmin Li Xidian Univ. impact, the semi-supervised method is introduced to correct the mislabeled samples. The proposed method is Fault diagnosis is an important branch of modern based on the basic principle that the characteristics of control system and plays an important role in industrial samples of the same category are more similar than production. Researchers keep pursuing more those of samples of different categories, so in the convenient and practical methods for fault diagnosis. As feature space, the number of samples of the same data collection becoming more convenient, data-driven category around a sample is more than that of different methods develops rapidly for their excellent categories. Cross validation is used to divide the performance, especially the deep learning methods have training set into sub training set and validation set, and been a popularity way to fault diagnosis. This paper tries the samples in the validation set are regarded as to use two data-driven methods (ResNet-50 and SE- unlabeled, k nearest neighbor (KNN) classifier label the ResNet-50) on fault diagnosis without transfer learning. samples in the validation set according to the samples in More uniquely, training data and testing data are the sub training set. Because there are mislabeled collected under different conditions. SE-ResNet-50 samples in the sub training set, it is difficult for KNN achieves the highest accuracy 99.1%, which is better classifier to label all samples in the validation set than ResNet-50 obviously. This experiment shows that correctly at one time. So we need to use the above SE-ResNet-50 achieves good performance without large method iteratively. Thus, the mislabeled samples in the training samples in fault diagnosis. training set is basically corrected. Experiments on the ECG signal corrected from the MIT-BIH arrhythmia 14:30-14:50 SunB03-4 database show the effectiveness of the proposed method. A Novel Hybrid Fault Diagnosis Method Based on EWT-SA-PNN 15:10-15:30 SunB03-6 Dazi Li Beijing Univ. of Chemical Tech. Xuejia Zhang Beijing Univ. of Chemical Tech. A Multi-channel Multi-head CNN Framework for Fault Xin Ma Beijing Univ. of Chemical Tech. Classification in Industrial Process Qibing Jin Beijing Univ. of Chemical Tech. Hui Wu Huazhong Univ. of Sci. & Tech. Yan Wang Zhengzhou Univ. of Light Industry In order to improve the efficiency and accuracy of fault Junyu Lin Huazhong Univ. of Sci. & Tech. diagnosis, a novel fault diagnosis method based on Weidong Yang Huazhong Univ. of Sci. & Tech. signal processing and artificial intelligence method is Yanwei Wang Wuhan Institute of Tech. proposed in this paper, which mainly combines Ying Zheng Huazhong Univ. of Sci. & Tech. empirical wavelet transform (EWT) with probabilistic neural network (PNN). A reference frequency method is This paper proposes a novel fault classification method proposed to make the input signals contain more via convolutional neural network with multi-channel and complete feature information, which solved the problem multi-head along the time dimension, which is defined as of incomplete features in low frequency fault signals in MM-CNN. The MM-CNN extracts features of industrial practical industrial process. Firstly, the signal process data by convolutional layers from local to global reconstruction is realized by EWT, thus the feature level. Unlike traditional methods, this method can information in the signal is strengthened. Then, capture the independent features of every process according to the 15 features commonly used in the variable and the relevant characteristics from sensor signal, several feature parameters with high sensitivity data to learn more useful latent fault patterns. Besides, a are selected as features through sensitivity analysis data preprocessing approach is proposed to transform (SA). Finally, the signal features and corresponding tags original data for convolutional neural network. Finally,

DDCLS’20 for all the all 21 faults in Tennessee Eastman (TE) The demand for high-precision positioning is increasing process, this paper compares the proposed method with in modern marine applications such as marine the published methods and a architecture which engineering, marine transportation, marine energy combines the convolutions of multi-head and a development, and marine monitoring. The improvement bidirectional Long Short-Term Memory (Bi-LSTM) layer of China’s BeiDou Navigation Satellite System in fault classification. The simulation results show that (hereinafter referred to as BDS) with the capability of the method has better classification performance than global coverage service has made it possible to achieve the state-of-the-art methods. high-precision positioning of the ocean using BDS sea-based augmentation technology. In this paper a SunB04 Room 4 high-precision positioning terminal deployed on a IS：Data-driven identification, optimization and control BDS-equipped buoy is proposed, with emphasis on the 13:30-15:30 composition, key techniques, functions, and Chair: Zhe Gao Liaoning Univ. performance indicators involved. Multiple experiments CO-Chair: Lichun Yang Beihang Univ. have been conducted on the coastal area of the East China Sea in Zhoushan City, Zhejiang Province to test 13:30-13:50 SunB04-1 the positioning accuracy of the proposed terminal. The A Working and Verification Framework of Marine BDS experimental results have demonstrated that the average High Precision positioning accuracy based on the BDS sea-based Jieru Niu Jiangsu Automation Research Institute augmentation system can be achieved at centimeter Lichun Yang Jiangsu Automation Research Institute accuracy level, which meets the high-precision Xin Song Jiangsu Automation Research Institute positioning requirements of marine users. These results also indicated the promising potential of BDS to provide BDS high precision navigation and positioning system comprehensive functions and optimal performance. plays an important role in marine economy with the “B&R” strategy. This paper focus on marine high 14:30-14:50 SunB04-4 precision positioning system based on BDS A Finite Time Neural Network Model for Solving improvement technology, communication network Time-varying Matrix Inequality Problem design and surface/under water precision positioning Tanglong Hu Zhejiang Univ. of Sci. & Tech. technology, design the marine BDS high precision Junwen Zhou Zhejiang Univ. of Sci. & Tech. positioning test and verification system. An experiment Ying Kong Zhejiang Univ. of Sci. & Tech. is designed to verify that the system can effectively improve the positioning accuracy of the Beidou system. Time-varying matrix inequalities are frequently encountered in many mathematical calculations and 13:50-14:10 SunB04-2 engineering applications. To solve time-varying Learning rule with fractional-order average momentum problems in an effective way, a special recursive Zhang based on Tustin generating function for convolution neural network (ZNN) is proposed. However, the neural networks convergent time of ZNN tends to be infinity. To Jing Jian Liaoning Univ. accelerate the convergent speed, a recurrent neural Zhe Gao Liaoning Univ. network model with finite convergent property (FTNN) is presented and is used to solve the linear time-varying Tao Kan Liaoning Univ. matrix inequality problem. Additionally, convergence and stability of the proposed FTNN model are analyzed. In this paper, we propose a fractional-order average Finally, simulations about the FTNN network model momentum (FOAM) method based on Tustin generating shows that the convergence performance of FTNN function to train parameters in convolution neural model is superior than that of ZNN model. networks. Taking the classical data set MNIST as the training and testing data, the effectiveness of the FOAM 14:50-15:10 SunB04-5 for CNNs is verified. The experimental results show that A Novel Attracting-Law based Digital Control Strategy the stochastic gradient descent method based on FOAM with Improved Convergence Rate and Steady-State Error can improve the recognition accuracy and learning Lingwei Wu Taizhou Univ. convergence speed of convolution neural networks. Mingxuan Sun Zhejiang Univ. of Tech. 14:10-14:30 SunB04-3 In this paper, a novel attracting law with improved A new Buoy-based BDS High-precision Positioning convergence rate and steady-state error band is Terminal: Design and Experimental Results presented for uncertain discrete-time systems, which Lichun Yang Beihang Univ. applies the tracking error itself for the control design. Jieru Niu Jiangsu Automation Research Institute The attracting law is designed based on the monotone Xin Song Jiangsu Automation Research Institute increasing continuous function taking its value between

0 and 1 for the absolute value of the tracking error. The the signed network achieve the bipartite consensus and disturbance compensation is introduced in the attracting state stability within a finite time when its associated law, by which the controller is designed to ensure faster signed digraph is structurally balanced and unbalanced, convergence and better robust stability of the respectively. With the help of improved Lyapunov closed-loop system. For characterizing the tracking potentials, the convergence analysis can be further performance, detailed results of both the absolute derived for the proposed protocol. Two simulation attractive layer bound and the steady-state error band examples are introduced to demonstrate the are given. The validity of the proposed approach is effectiveness of our developed results. confirmed by simulation results. 13:50-14:10 SunB05-2 15:10-15:30 SunB04-6 Finite-time Annular Domain Stabilization for Nonlinear Locality-Constrained Dictionary Learning Classification Systems in Strict-feedback Form Method of WCE Images Zhiguo Yan Qilu Univ. of Tech. Zixin Shen Zhejiang Univ. of Tech. Shandong Univ. Yue Ma Zhejiang Univ. of Tech. Dongkang Ji Qilu Univ. of Tech. Jinhui Zhu The Second Affiliated Hospital of Zhejiang Guolin Hu Qilu Univ. of Tech. Univ. School of Medicine Mingjun Du Qilu Univ. of Tech. Sheng Li Zhejiang Univ. of Tech. Xiaoping Liu Lakehead Univ. Liping Chang Zhejiang Univ. of Tech. Qianru Jiang Zhejiang Univ. of Tech. The problem of finite-time annular domain stabilization Xiongxiong He Zhejiang Univ. of Tech. for nonlinear systems in strict-feedback form is considered in this paper. Firstly, a definition of finite-time Wireless capsule endoscopy (WCE) is widely used in annular domain stability for nonlinear systems is given medical diagnosis which generates lots of images for and also some practical explanation is presented. each operation. For clinicians, analyzing these images is Secondly, a back-stepping design approach is proposed a time-consuming and laborious task. Therefore, an and a sufficient condition for the existence of finite-time automatic image classification algorithm is proposed to annular domain stabilization controller is given. Finally, help doctors quickly check the condition of specific a detailed example is used to show back-stepping organs such as stomach or small intestine in the design approach superiority to linear matrix inequality digestive tract. In the preprocessing stage, we obtain the approach. regions of interest from the images of cecum, pylorus and cardia followed by extracting the fused color and 14:10-14:30 SunB05-3 texture features. At the next stage, an effective organ Finite-Time Annular Domain Stability and Stabilization of classification method based on dictionary learning was Discrete-Time Stochastic Systems proposed, the locality-constrained term was constructed Zhiguo Yan Qilu Univ. of Tech. using the profile matrix and locality information of Shandong Univ. atoms. The simulation results demonstrate that the Qingfeng Lv Qilu Univ. of Tech. LCDL algorithm exceeds some existing algorithms on Guolin Hu Qilu Univ. of Tech. the organ classification task. Mingjun Du Qilu Univ. of Tech. SunB05 Room 5 Qiongying Kong Qilu Univ. of Tech. IS ： Distributed learning and control of networked systems (I) 13:30-15:30 This paper discusses the problems of the finite-time Chair: Mingjun Du Qilu Univ. of Tech. annular domain stability (FTAD-stability) and CO-Chair: Zhiguo Yan Qilu Univ. of Tech. stabilization of discrete-time stochastic systems (DTSS). Shandong Univ. First, a definition of FTAD-stability for DTSS is given and a stability criterion is also proposed. Second, we 15:30-13:50 SunB05-1 respectively give some sufficient conditions for the existence of state feedback controller (SFC), static Finite-Time Bipartite Consensus Problems for Signed output feedback controller (SOFC) and dynamic output Networks With Directed Topologies feedback controller (DOFC). Finally, a numerical example Mingjun Du Qilu Univ. of Tech. is utilized to show the effectiveness of proposed Zhiguo Yan Qilu Univ. of Tech. method. Shandong Univ. 14:30-14:50 SunB05-4 This paper concentrates on investigating the finite-time Robust Iterative Learning Control for Continuous-Time bipartite consensus problems of directed signed Systems With Nonrepetitive Model Uncertainties networks subject to both cooperative interactions and Jingrao Zhang Beihang Univ. antagonistic interactions. Based on the nearest neighbor Deyuan Meng Beihang Univ. rule, a distributed control protocol is proposed to make 56

DDCLS’20

For iterative learning control (ILC), one of the basic pattern moving. First of all, the way of system dynamics assumptions is the strict model repetitiveness and description based on pattern moving is introduced. however one crucial issue left to be addressed is how to Then, according to the mentioned way a deal with the nonrepetitive model uncertainties. This pattern-moving-based model free adaptive control crucial problem motivates our paper, where the robust (PMFAC) algorithm is put forward, and pseudo orders of convergence analysis can be implemented for linear the PMFAC algorithm are identified by using the continuous-time systems with nonrepetitive model conditional entropy of output classes. At last, simulation uncertainties. It is shown that the system trajectories are results demonstrate the feasibility and effectiveness of ensured to be bounded and then the robust output proposed data-driven control method. tracking of a specified trajectory can be realized in spite SunB06 Room6 of a small residual error whose bound depends IS ： Identification and adaptive control for nonlinear continuously on those of the nonrepetitive uncertainties. mechanical systems 13:30-15:30 A simulation example is provided to illustrate the Chair: Jing Na Kunming Univ. of Tech. obtained robust ILC results. CO-Chair: Zhenhua Wang Harbin Institute of Tech. 14:50-15:10 SunB05-5 13:30-13:45 SunB06-1 A Simplified Data-driven Optimal Iterative Learning Modeling and Simulation of Signal Acquisition System Control based on Iterative Extended State Observer Based on Inhibitor Arcs Hierarchical Coloured Petri Nets: Wei Ai South China Univ. of Tech. Taking Dust Signal Acquisition System as an Example Peilin Lin South China Univ. of Tech. Shenglei Zhao Shandong Univ. of Sci. & Tech. Xiangyang Li South China Univ. of Tech. Jimingli Li Shandong Univ. of Sci. & Tech. Mingyue Tan Shandong Univ. of Sci. & Tech. Inspired by Active Disturbance Rejection based Iterative Chuannuo Xu Shandong Univ. of Sci. & Tech. Learning Control (ADR-ILC) and Data-Driven Optimal Xuezhen Cheng Shandong Univ. of Sci. & Tech. Iterative Learning Control (DDOILC), this paper proposes a simplified data-driven optimal iterative control method The development of existing signal acquisition systems based on iterative extended state observer (IESO). has long-term and high-cost problems. To deal with such Accurate estimation of the system uncertainties is situation, this paper takes the dust signal acquisition observed by IESO during the iterative process. Though system as an example, and proposes a modeling method considering the uncertainties on iterative dynamic combining Information Flow Hierarchical Dynamic linearization method, it is not needed to deduce a new (IFHD) with Inhibitor arcs Hierarchical Coloured Petri form of the original iterative pseudo partial derivative Nets (IHCPN). The method first establishes a Unified (PPD). IESO, undertaking as the tool to estimate the Modeling Language (UML) model based on the whole uncertainties, is added into the DDOILC control functional block diagram of the system. To simplify the law as a separate part. The whole control law is more reference model, improved the transformation rules intuitive and concise than other IESO based DDOILC between the UML model and Petri Nets (PN) model. To method which has modified PPD updating law and ensure the validity, safety, and rationality of the control law. At the same time the variable gain control reference model, the dynamic analysis of the mechanism makes the proposed method demonstrate constructed PN model is carried out by using the superiority over ADR-ILC in the case of strong analysis method of the reachable marking graph. The nonlinearity. Simulation shows it that can achieve better simulation results of CPN Tools show that the model performance than DDOILC and the other IESO based satisfies boundedness, reachability, liveness, and DDOILC. fairness, conforms to the performance indicators of system operation. Compared with the modeling method 15:10-15:30 SunB05-6 of Hierarchical Coloured Petri Nets (HCPN), IHCPN can A Pattern-moving-Based Data-driven Control Method for greatly reduce the number of model nodes and A Kind of Industrial Production Processes connecting arcs, reduce the complexity of the system Xiangquan Li Univ. of Sci. & Tech. Beijing model, and provide a reliable reference model while Zhengguang Xu Univ. of Sci. & Tech. Beijing effectively save system development time and cost. Mushu Wang Shandong Jiaotong Univ. Jiarui Cui Univ. of Sci. & Tech. Beijing 13:45-14:00 SunB06-2 Xu Yang Univ. of Sci. & Tech. Beijing Comparison of Three Different Wheeled-hopping Robots Lufeng Zhang Beijing Institute of Tech. Due to the existence of a kind of industrial production Xuemei Ren Beijing Institute of Tech. process which is too complex to determine the model structure and identify the system parameters, a Hopping robot is playing an important role in the data-driven control method is proposed based on exploration on bumpy road. This paper presents three

57 different wheeled-hopping robots all designed by fins and hydrodynamic force/torqu, the dynamic model ourselves. Firstly, the mechanical structure of the three of this UBVMS is established. The position control different wheeled-hopping robots is introduced. The problem is modeled into a continuous-state, corresponding advantages and disadvantages are also continuous-action Markov decision process (MDP) with discussed in our paper. Then, through the simulated a deterministic state transition algorithm based on the results we compare these three different designs on the dynamic model. To solve this MDP, a reinforcement hopping performance and the factors affecting the learning method is presented, which is based on the motion. Finally, we’ll conclude and show our thoughts deep deterministic policy gradient (DDPG) theorem. The about the future development on the wheeled-hopping simulations of the position control in 5 cases are shown robot. in the end.

14:00-14:15 SunB06-3 14:30-14:45 SunB06-5 Fault Diagnosis of Wind Motor based on Convolutional Variable Gain Accurate Differential Compensation Neural Network Control for A Class of Strict Feedback Systems Yakun Wang Harbin Institute of Tech. Guofa Sun Qingdao Univ. of Tech. Hui Yin Harbin Institute of Tech. Xingrong Zhang Qingdao Univ. of Tech. Zhenhua Wang Harbin Institute of Tech. Yi Shen Harbin Institute of Tech. In this paper, an adaptive fuzzy controller is proposed to a class of discrete-time nonlinear systems with strict This paper studies the fault diagnosis of wind motors, feedback. In these systems, total disturbance consists which is an important way to improve the safety and the fuzzy approximation error and unknown external reliability of wind motors. It is non-trivial to extract the disturbance. And the proposed accurate disturbance fault features from the original vibration signals by the observer could track the approximation disturbance traditional methods. We propose a novel method to accuratly. It can be proved via the Lyapunov theorem improve the fault diagnosis performances of wind that all signals in this closed-loop system are motors. First, the Wigner-Ville distribution method is guaranteed to be bounded. Finally, the simulation used to generate the time-frequency images of the example demonstrates the effectiveness of the proposed vibration signals in different speed ranges of the motor, scheme. which is helpful for fault features extraction. Then, we use the convolutional neural network, an important tool 14:45-15:00 SunB06-6 in the field of deep learning, to extract the fault features Real-time Indoor Object Detection Based on Deep from the time-frequency images. Finally, simulation Learning and Gradient Harmonizing Mechanism results based on the measurement data of an actual Min Chen Beijing Institute of Tech. wind motor are provided to demonstrate the Xuemei Ren Beijing Institute of Tech. effectiveness of the proposed method. Zhanyi Yan Beijing Institute of Tech.

14:15-14:30 SunB06-4 Due to the indoor environment is complicated, and the Position Control of an Underwater Biomimetic proportion of the background is much larger than the Vehicle-Manipulator System via Reinforcement Learning object, the positive and negative categories of the Ruichen Ma Univ. of Chinese Academy of Sci. sample are not balanced. This paper proposes that the Institute of Automation, Chinese Academy imbalance of sample categories is due to the imbalance of Sci. of difficult and easy samples which can be reflected by Yu Wang Institute of Automation, Chinese Academy the gradient norm of the sample. The gradient density is of Sci. introduced to solve the uneven distribution of gradient Zisen Gao Beijing Institute of Petrochemical Tech. norm. The parameter “gradient density” is added to the Tianzi Zhao Beijing Institute of Petrochemical Tech. Yolov3 loss function which is one of the best one-stage Rui Wang Institute of Automation, Chinese Academy object detection frameworks that can balance accuracy of Sci. and speed in order to improve the sample category Shuo Wang Univ. of Chinese Academy of Sci. imbalance. Considering the high computational Institute of Automation, Chinese Academy complexity of the sample gradient density, the gradient of Sci. norm is divided into equal-width intervals, and the same Chao Zhou Naval Research Academy gradient density is adopted for the samples falling in same region so as to simplify the calculation and This paper addresses a position control method of an improve the training efficiency. The experimental results underwater biomimetic vehicle-manipulator system demonstrate that the improved approach achieves best (UBVMS) through reinforcement learning. The system detection accuracy and obtains the most accurate description of the UBVMS with undulating fins is given. bounding boxes of indoor objects to be classified with Considering the force/torqu generated by undulating quick speed. Therefore, the gradient harmonizing

DDCLS’20 mechanism of samples can improve the sample rehabilitation training robot matches the actual nonlinear category imbalance. The improved indoor object system model. The STF algorithm has better tracking ability, detection algorithm can be applied in the field of high control accuracy and smoother operation. The research intelligent indoor monitoring, indoor service robots. results provide a reference for the research on the servo system of lower limb rehabilitation equipment. 15:00-15:15 SunB06-7 SunB07 Poster Hall Adaptive Output Feedback Control for a Multi-Motor Iterative session (I) 13:30-15:30 Driving System with Completely Tracking Errors Chair: Youqing Wang Shandong Univ. of Sci. & Tech. Constraint CO-Chair: Xin Deng Chongqing Univ. of Posts & Minlin Wang Changcheng Institute of Metrology & Telecommunications Measurement, Beijing Institute of Tech. Xueming Dong Changcheng Institute of Metrology & 13:30-15:30 SunB07-1 Measurement. Optimal Predictive Delay Compensation for Robot Xuemei Ren Beijing Institute of Tech. Networked Control system Dihan Chen Xihua Univ. This paper proposes an adaptive output feedback Xia Liu Xihua Univ. controller for the multi-motor driving system (MDS) to achieve the precision motion control with completely In order to solve the problem of random delay in robot tracking errors constraint. By adopting a K-filter networked control system, an optimal predictive observer to estimate the unknown system states, a compensation control method is designed. The state modified barrier Lyapunov function (MBLF) is integrated observer is used to observe the joint position into the adaptive output feedback control to make all the information of the robot at the current time, and it is sent tracking errors constrained within the prescribed to the control predictive generator. The predictive bounds. Since the MBLF is suitable for both constrained generator forecasts and calculates the control input at and unconstrained conditions, it expands the application the later time, and optimizes the predictive result of the filed of the classical Lyapunov function. Moreover, control input by rolling optimization. Finally, simulations minimize learning parameter technique is utilized into are conducted on a two-joint robot networked control the adaptive law design, which improves the adaptive system. The simulation results show that the method learning process greatly. The system stability is proven can effectively compensate for the influence of random by Lyapunov theory. The simulations are conducted on a delay and packet disorder on the position tracking four-motor driving system to illustrate the efficiency of performance of the system. the proposed controller.

13:30-15:30 SunB07-2 15:15-15:30 SunB06-8 Root Cause Diagnosis Framework Based on Granger Approach of driving system for lower limb rehabilitation Causality with the Combination of Normal and Fault Data training robot based on strong tracking filtering Xiangyun Ye Shanghai Univ. Dawei Jiang Changchun Univ. of Sci. & Tech, Jianguo Wang Shanghai Univ. Changchun Univ. of Tech. Fei Wang Shanghai Univ. Guoquan Shi Changchun Univ. of Sci. & Tech. Yuan Yao National Tsing-Hua Univ. Bing Zhang Jilin Univ. Junjiang Liu Baoshan Iron and Steel Co., Ltd. Yuan Tian Changchun Vocational Institute of Tech. Granger causality analysis is one of the most widely To solve the problem of lowering the control accuracy of the used methods in root cause diagnosis. This method can robotic servo system of lower limb rehabilitation training due get effective results in many cases, but there are still to inaccurate models and load disturbances, this paper takes some problems and underutilization of data is one of the one degree of freedom (DOF) lower limb rehabilitation them. Granger causality analysis only used the fault training robot servo system as the research object, and relate data segment. This paper proposes a novel root establish a mathematical model of the servo system of the cause diagnosis framework based on Granger causality lower limb rehabilitation training robot including several analysis, and attempts to combine the normal and fault subsystems such as the execution system, feed system and data to make the result more accurate. The main ideal is drive system. A control algorithm for driving system of lower to test the change of causality intensity before and after limb rehabilitation training robot based on strong tracking the fault to optimize the result of the fault propagation filter (STF) is adopted. Through simulation analysis, the paths. Tennessee Eastman (TE) process data and TE stability of STF algorithm is compared, which shows that the data was used to verify the effectiveness of the method. proposed algorithm has the advantages of fast tracking speed, noise suppression 13:30-15:30 SunB07-3 and high accuracy. The experimental results further show that the established servo system model of the lower limb

Power-sum Activated Neural Dynamics for Lower Limb An Unmanned Vehicle Trajectory Tracking Method Motion Intention Recognition Based on Improved Model-free Adaptive Control Algorithm Jiachang Li Lanzhou Univ. Dongdong Yuan Beijing Institute of Tech. Yimeng Qi Lanzhou Univ. Yankai Wang Beijing Institute of Tech. Mei Liu Lanzhou Univ. Shuai Li Lanzhou Univ. In order to solve the dynamic modeling and parameter Zhongbo Sun Changchun Univ. of Tech. identification problems of unmanned vehicles trajectory Long Jin Lanzhou Univ. tracking control, a mathematical model of unmanned vehicle trajectory tracking is designed based on the It can be found that the exact solution of the equations data-driven model-free adaptive control method, which has quite similarity to the pursuing of the minimum error does not depend on the precise dynamic model of the in the field of control. Therefore, many control problems unmanned vehicle. The model-free adaptive control can be explained from the perspective of solving method is extended to the unmanned vehicle trajectory equations. As a powerful computing tool, neural tracking control, and the model-free controller is dynamics model has been widely applied in addressing designed and applied to the driverless vehicle trajectory time-varying equations and optimization problems tracking control. Aiming at the problem that the general because of its remarkable convergence and robustness. compact form dynamic linearization model-free adaptive For neural dynamics model, the selection of activation control (CFDL-MFAC) algorithm cannot converge in function plays a significant role in its performance. In vehicle trajectory tracking control, combined with the this paper, a special nonlinearly activated (i.e. dynamic characteristics of unmanned vehicles, an power-sum activation function) neural dynamics model improved model-free adaptive control algorithm is is exploited to the experimental simulation of intention proposed in this paper. The simulation results verify the recognition of lower limb motion. Compared with the effectiveness and feasibility of the algorithm. linear activation function, the corresponding analyses of Mathematical simulation results show that the improved convergence and robustness are given respectively, model-free adaptive algorithm of the designed demonstrating the superior characteristics of the neural unmanned vehicle is effective and can effectively dynamics model with power-sum activation function. implement the trajectory tracking control of the unmanned vehicle. At the same time, the design of the 13:30-15:30 SunB07-4 controller does not depend on the kinematics and A Novel Group Consensus Protocol with Hybrid Delay dynamics models of the unmanned vehicle, and it has for Linear Multi-agent Systems high control accuracy. Weixun Li Tianjin Univ. of Tech. & Education Liqiong Zhang Tianjin Univ. of Tech. & Education 13:30-15:30 SunB07-6 Limin Zhang Zhongyuan Univ. of Tech. P-th Moment Consensus of the Disturbed Multi-agent Systems In allusion to the issues of achieving group consensus Hui Wang Xidian Univ. of ecumenical multi-agent systems, a neoteric and Junmin Li Xidian Univ. comprehensive control protocol was designed Chao He Xidian Univ. neighbor-based messages of agents and distributed management of the neighborhood, which includes the In this paper, pth moment leader-following consensus of three situations of the general communication delay, multi-agent systems (MASs) with additive measurement lagged communication delay and no communication noises and time-varying communication delays is delay. Then, on account of the designed control protocol investigated. A new consensus protocol with a and the related knowledge of the algebra and diagram time-varying gain is designed. What’s more, a sufficient theory, group consistency matter of multi-agent systems condition of pth moment consensus of leader-following are transformed into stability matters of deviation MASs is also given. Finally, an example is provided to systems. Via using the knowledge of Lyapunov stability demonstrate the effectiveness of the proposed theory, we obtain a sufficient and indispensable criterion algorithms. to ensure that multi-agent system can implement group consistence. Afterwards, theory consequences were 13:30-15:30 SunB07-7 numerically simulated in the aided by mathematical software, and the consequences show that the On the Equivalence between PID-like Controller and presented manipulative protocol capable multi-agent LADRC for Second-order Systems systems to implement multi-group consensus under Xiangyang Li South China Univ. of Tech. certain conditions. Zhiqiang Gao Cleveland State Univ. Wei Ai South China Univ. of Tech. 13:30-15:30 SunB07-5 Senping Tian South China Univ. of Tech.

In this paper, the equivalence is established between the

DDCLS’20 linear active disturbance rejection control (LADRC) and obtained by calculating the transfer entropy between the two-degree of freedom (2-DOF) sequences, and the root cause of the fault can be found. proportional-integral-derivative (PID) with lead-lag The effectiveness and accuracy of the method are compensators. This allows advantage of LADRC, verified by simulations and actual industrial cases particularly its bandwidth-parameterization strategy, to (Tennessee-Eastman process). be incorporated into the run-of-mill PID controllers. Specifically, the competing design objectives in 13:30-15:30 SunB07-10 disturbance rejection, tracking, and noise sensitivities Forecasting Sudden Changes in Telemetry Data from can now be handled with ease. Satellite Control System Mengxin Shi Chinese Academy of Sci. 13:30-15:30 SunB07-8 Jiasen Yang Chinese Academy of Sci.

Zonotope-based H-/L∞ Fault Detection Observer Design Chunmei Wang Chinese Academy of Sci. for Linear Systems over Sensor Network Xin Meng Chinese Academy of Sci. Jing Wang Beijing Univ. of Chemical Tech. Zhenlin Wang Beijing Univ. of Chemical Tech. Classical multi-step telemetry data prediction methods Meng Zhou North China Univ. of Tech. have low accuracy when predicting data patterns with sudden changes, and can be hardly applied to fault In this paper, the problem of distributed fault detection detection. We propose a Composite LSTM Autoencoder based on zonotope is investigated for a class of Model with Attention Mechanism (CLAMA) to realize discrete-time linear system monitored by a sensor multi-step prediction of satellite telemetry data patterns network. Considering the existence of disturbance and with sudden changes. The proposed method learns actuator fault as well as the sensor anomaly, a correlated multivariate parameters, and automatically distributed H-/L∞ multi-objective observer according to extracts features and predicts telemetry sequence. The the information of individual sensor and its neighboring result of experiments based on data sampled from nodes is designed, which makes the generated residual quantum satellite shows that the proposed method has signal is robust against disturbance and sensitive to smaller deviation in the root mean square error (RMSE) system fault as well as local sensor fault. In addition, the comparing with the vector autoregression (VAR) model residual boundaries of sensors are calculated by and support vector regression (SVR) model, it can zonotopes, and a fault detection logic is proposed to effectively predicts sudden changes in telemetry data, achieve fault detection as well as discriminate the and has higher accuracy for multi-step prediction, thus sensor local fault from system fault. Finally, a simulation support ground operators’ decision making on fault case is adopted to demonstrate the effectiveness of the detection. proposed method. 13:30-15:30 SunB07-11 13:30-15:30 SunB07-9 Fault Detection of Rotating Machinery Based on Wavelet Causal Network Analysis and Root Cause Detection Transform and Improved Deep Neural Network Based on Parameter Variable Sequence Transfer Mingliang Cui Shandong Univ. of Sci. & Tech. Entropy Youqing Wang Shandong Univ. of Sci. & Tech. Guoqiang Zhao Shanghai Univ. Jianguo Wang Shanghai Univ. In the operation of wind turbine, gearbox faults are very Zeng Chen Shanghai Univ. common. It is very important to detect the fault Yuan Yao National Tsing-Hua Univ. effectively to ensure the safe and reliable operation of Chao Xu Baoshan Iron and Steel Co., Ltd. wind turbine. In this study, the wavelet analysis method is combined with an improved convolutional neural Transfer entropy (TE) is a data-driven, model-free network and support vector machine (CNN-SVM), and method that can obtain causal relationships between the proposed method is applied to the fault detection variables and is used in the modeling, monitoring, and and classification of the gearbox in the wind power fault diagnosis of complex industrial processes. generation equipment in the laboratory. The Transfer entropy can detect the causal relationship experimental results show that the proposed method between variables without the assumption of any basic achieves super classification result. model, but it is complicated and takes a long time to calculate. In order to solve this limitation, this paper 13:30-15:30 SunB07-12 proposes a method of causal network analysis and root A Research Method of Vibration Stationarity Based on cause detection based on parameter variable sequence Correlation Coefficient transfer entropy, which is more robust than traditional Bo Ma Beijing Univ. of Chemical Tech. transfer entropy, faster in calculation speed, and strong Libing Liang Beijing Univ. of Chemical Tech. in anti-interference ability, thereby improving the causal Zeyong Tao State Nuclear Power Plant Service Co. path accuracy. The causal network diagram can be Weidong Cai Beijing Univ. of Chemical Tech. 61

Due to the influence of various factors during the Zhongdang Yu Liaoning Petrochemical college operation of mechanical equipment, the vibration appears the characteristics of non-stationarity. In order As an effective data dimensionality reduction technique, to analyze the correlation of other monitoring indicators PCA is widely used in the field of process monitoring. that cause vibration non-stationarity, it is necessary to PCR, as an improved method of PCA, obtains the study the vibration stationarity. A research method of coefficient matrix between input and output by least vibration stationarity based on correlation coefficient is square regression of score matrix and quality variable. proposed in this paper. Firstly, the correlation coefficient However, the detection effect of PCR on quality-related of vibration data and corresponding time is calculated faults still needs to be improved. Focusing on this issue, by sliding to reflect the vibration trend. Secondly, the a MPCR method for quality-related fault detection is fluctuation threshold is determined by statistical proposed in this paper. Where LU decomposition is characteristics. Finally, the vibration stationarity is introduced to further decompose the coefficient matrix tested. The test results of vibration data of nuclear main of PCR, decompose process variables into pump show that the method proposed in this paper can quality-related and quality-independent parts, and better test the vibration stationarity, which lays a design corresponding test statistics to make the foundation for the study of the factors related to the algorithm more suitable for modern industrial systems. operating states of mechanical equipment. The effectiveness of the algorithm is verified by a numerical example and the Tennessee Eastman process. 13:30-15:30 SunB07-13 The results show that MPCR algorithm has higher fault EEG Identification Based on Brain Functional Network detection rate and better tracking performance than the and Autoregressive Model traditional PLS. Sijia Zhao Chongqing Univ. of Posts & Telecommunications 13:30-15:30 SunB07-15 Ke Liu Chongqing Univ. of Posts & Fault Diagnosis of Analog Circuit Based CS_SVM Telecommunications Algorithm Xin Deng Chongqing Univ. of Posts & Xinglong Yu Bohai Univ. Telecommunications Aihua Zhang Bohai Univ. Wei Mu Bohai Univ. Due to the advantages of high concealment, Xing Huo Bohai Univ. non-stealing and liveness detection, electroencephalography (EEG) is a promising biometric Abstract: In order to realize the classification of analog technology for security requirements and personal circuit fault modes, a fault diagnosis method based on identification. Many EEG identification method focuses CS_SVM algorithm is proposed. Firstly, by using the on specific task. The generalization of these methods to meta-heuristic Cuckoo Search (CS) technique, the other tasks or datasets is limited. In this work, we optimal solutions which c and g of the target kernel propose a new EEG identification method, which is function are extracted. Next, the Support Vector Machine based on the combination of brain functional network (SVM) uses the radial basis kernel function of RBF and and autoregressive model, under different tasks. the obtained optimization parameters c and g to map the Specifically, we employ Phase Locking Value (PLV) data to a high-dimensional space to make it as linearly between paired EEG channels to construct the separable as possible. Finally, a model is established to functional connectivity matrix. Using a data-driven classify and judge each category. The Monte Carlo threshold, we get the corresponding binarized brain simulation data of Sallen-Key low-pass filter were network. Then the degree of each node is obtained by verified, and the MATLAB simulation results show that graph theory. Additionally, the autoregressive (AR) the classification accuracy of this method for nine coefficients are employed using Burg algorithm. The different circuit states is more than 99.3%, which feature vector is formed combined these AR coefficients improves the diagnostic performance of the classifier and node degrees. The support vector machine (SVM) is model. used to accomplish the EEG personal identification task. Experimental results on three public datasets with 13:30-15:30 SunB07-16 different tasks show that the proposed method has Safety Risk Assessment of Ship-based Test System superior performance in terms of classification accuracy Based on Fuzzy Analytic Hierarchy Process and stability. Jing Wang China State Shipbuilding Corp. Ltd. Wei Zheng China State Shipbuilding Corp. Ltd. 13:30-15:30 SunB07-14 Huihui Zhang China State Shipbuilding Corp. Ltd. A Modified Principal Component Regression Method for Tianyong Deng China State Shipbuilding Corp. Ltd. Quality-related Fault Detection Wenxiao Gao Bohai Univ. In order to evaluate the safety risks and improve the Aihua Zhang Bohai Univ. safety and success rate in the ship test system, a safety

DDCLS’20 evaluation method for the ship-based test system based developed by a wheeled mobile robots (WMR) for on the fuzzy analytic hierarchy process was proposed. trajectory tracking in the repetitive systems. The design Determining the safety risk assessment index system of and analysis of the controller only uses the I/O of the ship-based test system is the first step according to the system and in the absence of any explicit model principle of safety system. Then the weight of each risk information. Control performance is improved by using assessment index is determined through fuzzy analytic higher-order learning control methods to obtain more hierarchy process. Finally, a comprehensive assessment control information in the iterative process. The control is performed to determine the risk level combined with performance of the control scheme is proved by the expert scoring method and the weight of risk mathematical analysis and simulation. assessment indicators through an example. The evaluation results show that the evaluation system 13:30-15:30 SunB07-19 realizes the quantification of the risk assessment and DNN-based Implementation of Data-driven Iterative could provide theoretical guidance and technical Learning Control for Unknown System Dynamics reference for the risk analysis and safety management in Junkang Li Shanghai Univ. the ship-based test system. Yong Fang Shanghai Univ. Yu Ge Shanghai Univ. 13:30-15:30 SunB07-17 Yuzhou Wu Shanghai Univ. Grayscale-information-based Segmentation Registration for Fault Diagnosis of Train Components As the condition of iterative learning control, it is usually Zhaoxin Li South China Univ. of Tech. necessary to estimate the parameters of the system Guangzhou Metro Group Co., Ltd. model to determine whether the system satisfies the Ziyi Liu Southwest Jiaotong Univ. global Lipschitz condition and estimate the upper and Yuanjiang Hu Guangzhou Yunda Intelligent Tech. Co., Ltd. lower bounds of the rate of change of the system. Yiming Zhang Southwest Jiaotong Univ. However, for systems with unknown dynamics, the Zhengyi Liu Guangzhou Yunda Intelligent Tech. Co., Ltd. data-driven iterative learning control based on system Na Qin Southwest Jiaotong Univ. input and output cannot be realized fully. In this paper, using the nonlinear mapping and feature extraction Considering the long time consumption and high labor ability of deep learning, only input/output data is used to price in manual maintenance of train systems, determine whether the uncertain system satisfies the computer-vision-based maintenance schemes have global Lipschitz condition and estimate the upper and become more and more popular, which can detect the lower bounds of the system's rate of change, so as to abnormality of train components efficiently by virtue of realize the iterative learning control of the system. The train images. Nevertheless, due to the complex simulation results verify the validity of estimating acquisition environment, different degrees of distortion whether the system satisfies the ILC condition only occur frequently in the collected images, affecting the based on the input/output data of the system. anomaly detection of train. In this paper, a grayscale-information-based segmentation method is 13:30-15:30 SunB07-20 proposed for train image registration, aiming at Adaptive Iterative Learning Backstepping Control for intelligent fault diagnosis of train components. The Nonlinear Strict-feedback Systems novelties of the scheme lie in that it is able to correct the Jianyong Chen Wenzhou Vocational College distortion promptly, eliminate the distortion of image of Sci. & Tech. content accurately, and lay a foundation for the Zhejiang Univ. of Tech. subsequent anomaly detection. At last, the efficacy of the registration scheme is verified for the whole train In this paper, an adaptive iterative learning control image (2048 × 180000 pixels) in the sense of mean strategy is proposed for a class of nonlinear square error, normalized cross-correlation matching as strict-feedback systems in the presence of arbitrary well as structural similarity. initial state error. The presented control algorithm utilizes an expected error trajectory to resolve the initial 13:30-15:30 SunB07-18 condition problem. The controller is designed based on High-order Model-free Adaptive Iterative Learning the backstepping technique, and the unknown constant Control for Velocity Tracking of Wheeled Mobile Robots parameters are estimated via differential-difference Jiawei Li Qingdao Univ. of Sci. & Tech. learning law. A typical series is introduced to solve the Ronghu Chi Qingdao Univ. of Sci. & Tech. influence of external disturbance on tracking Dejing Yang Shandong Jinkaifeng Machinery performance. Theoretical analysis shows that all signals Tech. Co., Ltd. of the closed-loop system are bounded, and the system output perfectly follows the reference signal over the In this paper, a data-driven high-order model-free pre-specified interval. Finally, a simulation example is adaptive iterative learning control (HMFAILC) is provided to demonstrate the effectiveness of the

63 approach. the calculation results show the effectiveness of the proposed method. 13:30-15:30 SunB07-21 Energy Sub Hub Based Energy Flow Matrix Modeling 13:30-15:30 SunB07-23 and Its Application to Multi-carrier Energy System Data-driven Reconstruction for Massive Buildings within Binbin He Shanghai Univ. Urban Scenarios: A Case Study Li Jia Shanghai Univ. Ziye Chen National Univ. of Defense Tech. Feng Li Jiangsu Univ. of Tech. Xiaojia Xiang National Univ. of Defense Tech. Han Zhou National Univ. of Defense Tech. The aggravation of global energy crisis and Tianjiang Hu Sun Yat-sen Univ. environmental pollution impels people to speed up the research of energy utilization. The multi-carrier energy The reconstruction and visualization of urban system (MES) can greatly improve the energy utilization environments is widely used in immersive simulation, efficiency, the construction of Energy Hub (EH) model is but the massive data of buildings bring huge burden to a key in MES research. The paper proposed a novel the modelling and rendering process. This paper matrix modeling method for MES. Firstly, according to proposes an improved fast approach to build and the types of energy flow, energy hub is divided into visualize the scene graph based on the level of details different energy sub hubs, and then the detailed (LOD). Firstly, an automatic reconstruction scheme of modeling process of energy flow equation is given. An urban buildings is designed by using the public vector optimal scheduling model of MES is proposed to data set. Secondly, a tree-type organization and minimize the daily cost of energy. The typical summer management structure is constructed for urban days with energy storage devices and demand response scenarios inspired by the idea of recursive division. considered respectively are taken as cases study. Thirdly, a dynamic data scheduling strategy is developed Simulation results show that the method is effective and with concerns of self-organizing the generated 3D feasible for MES with different structures. The massive model data. The developed approach is called comparison of different cases shows that energy Detail Segmentation of Scene Tree Method, namely storage devices and demand response can decrease the DSSTM. Finally, experiments are conducted for cost very well. performance comparison between the proposed DSSTM and a conventional method, while both are realized by 13:30-15:30 SunB07-22 using the open source library, OpenSceneGraph, http://www.openscenegraph.org/. The experimental Data-driven Tie-line Scheduling Method results of reconstructing Changsha City have verified Zhi Cai China Electric Power Research Institute that the proposed method is effective and efficient for Sai Dai China Electric Power Research Institute practical applications of visualization on large-scale Yuhan Dai State Grid Sichuan Electric Power urban with massive buildings. The rendering speed is Supply Co. increased to 4~7 times of the conventional method, the Guofang Zhang State Grid Sichuan Electric Power dynamic loading time is shortened by 90%. Supply Co. Yi Lu State Grid Sichuan Electric Power 13:30-15:30 SunB07-24 Supply Co. Comparison of Three Data-Driven Identification Methods Tie-line scheduling in an interconnected power grid is and Experimental Testing on a YunZhou Unmanned difficult to perform optimization by establishing a unified Surface Vehicle physical model due to the different scheduling methods Xinshuang Lin Shandong Univ. of Sci. & Tech. in different dispatching centers. It may cause repeated Youqing Wang Shandong Univ. of Sci. & Tech. coordination and iteration in multi-level dispatching Mingliang Cui Shandong Univ. of Sci. & Tech. centers. Therefore, a data-driven tie-line scheduling method with self-learning ability is proposed. First, Unmanned Surface Vehicle (USV) plays an important role perform cluster pre-processing of historical dispatching in lots of areas such as military, hydrological survey, data with K-means algorithm; secondly, establish a deep salvage, rescue and exploration. Being different from learning model of tie-line scheduling based on long large-scale vessels, USV has the advantages of small short-term memory (LSTM), and build a mapping model volume, light weight, fast speed and easy control, and among system load, clean energy output and tie-line thus its modeling and parameter identification is schedule through historical data training; After that, the particularly important. In this paper, we first establish the all above process is used as a foundation to do the model for USV, and then identify the parameters of the tie-line scheduling; finally, continuously revise the model by employing least square method, recursive model by accumulating historical data, so that it has the least square method and particle swarm optimization ability of self-evolution and self-learning. We make a (PSO) algorithm, respectively. Comparisons are also case analysis based on the actual power grid data, and made between these three methods.

DDCLS’20

13:30-15:30 SunB07-25 improved algorithm for object contour detection in Extraction and Recognition of Device Graphics in natural scene based on image saliency. Firstly, the initial Process Flow Diagram contour is obtained by using the suppression effect of Wei Shao Qingdao Univ. of Sci. & Tech. non-classical receptive field, and the saliency map is Guangze Wang Qingdao Univ. of Sci. & Tech. obtained by using the hyper-complex Fourier transform Hongliang Xi Qingdao Univ. of Sci. & Tech. image saliency detection method, and then the initial Lei Feng Shandong Chaoyue Data Control contour and the saliency map are integrated into the Electronics Co., Ltd. final contour. Compared with the traditional contour detection method, the contour extraction algorithm Design analysis on process flow diagram can find the based on image saliency can remove more background logical error and improve efficiency. Existing software information and improve the accuracy and integrity of used to drawing the process flow diagrams (such as object contour extracted in natural scenes. AutoCAD or Visio) does not have recognition and analysis function. For the images output by software, 13:30-15:30 SunB07-27 this paper describes the method of extracting and Improve Spark-based Application Performance Using recognition the device graphics representing the valves, Minimizer gauges, etc. Since the sizes of same device graphics Jinda Wu Shanghai Univ. may be different, the device graphics have to be Li Deng Shanghai Univ. extracted and normalized. In extraction, the points which Lili Wang Shanghai Univ. are obtained by convolving the image with gradient Kexue Li Shanghai Univ. operators, are used to positioning the device graphics. Yakang Lu Shanghai Univ. Considering the difficulty of extraction and recognition, Yang Song Shanghai Univ. the devices graphics are classified by shape. The device graphics with a circle are extracted by circle detection SpaRC (Spark Reads Clustering) is a generic sequence and normalized according to radius. The other device clustering algorithm based on Spark, which provides a graphics are extracted, and normalized by the bounding scalable solution for billions of reads. However, SpaRC rectangle after contours shrink. In recognition, the measures the correlation between reads by employing improved template matching and the recognition method k-mer. This method can effectively complete computing based on structure are used separately to recognize tasks when the amount of data is small. However, as the different device graphics. After experimentation, the amount of data increases, the shortcomings of long method described in this paper can significantly improve running time and large memory resources are the recognition rate of various types of device graphics. increasingly prominent. Here we explored a sequence similarity measurement method to alleviate these 13:30-15:30 SunB07-26 problems by using minimizer to measure sequence A Contour Detection Method Based on Image Saliency similarity between reads, without long running time and Yongcai Pan Guangxi Univ. of Sci. & Tech. large memory resources. This method combines the Yuwei Zhang Guangxi Univ. of Sci. & Tech. minimizer measurement strategy and extracts the Qingzheng Liu Guangxi Univ. of Sci. & Tech. overlap rate information of reads to measure the Zhaobin Wu Guangxi Univ. of Sci. & Tech. sequence similarity between different reads, instead of Wei Hu Guangxi Univ. of Sci. & Tech. the traditional method using k-mer. Results indicate that Yanxia Wei Guangxi Univ. of Sci. & Tech. the method offers great improvement in clustering performance. Compared with the traditional k-mer The object contour detection in natural scenes is an method, this method can effectively improve the use of important research problem in computer vision. The memory resources by SpaRC. difficulty is that the integrity of contour extracted is interfered with a large number of texture edges in the 13:30-15:30 SunB07-28 background seriously. In recent years, some researchers An Edge-Cloud Synergy Integrated Security have introduced image contour detection method with Decision-Making Method for Industrial Cyber-Physical biological visual features and achieved preferable Systems results. Such as, based on the fact of visual outer region Hang Xing Huazhong Univ. of Sci. & Tech. suppression, they can suppress a certain amount of Chunjie Zhou Huazhong Univ. of Sci. & Tech. texture edge while extracting the contour of the object, Xinhao Ye Huazhong Univ. of Sci. & Tech. and obtain the proper contour edge. However, when this Meipan Zhu Huazhong Univ. of Sci. & Tech. method is used for contour detection in some image which the texture is similar to the contour, the texture With the introduction of new technologies such as cloud edge still cannot be removed well, and the result is computing and big data, the security issues of industrial unsatisfactory. In consideration of the merit and demerit cyber-physical systems (ICPSs) have become more of the feature suppressed method, we propose an complicated. Meanwhile, a lot of current security

65 research lacks adaptation to industrial system upgrades. manipulator, collision detection is performed in In this paper, an edge-cloud synergy framework for Cartesian space by solving forward kinematics, and then security decision-making is proposed, which takes the Bezier curve is used to smooth the path. The advantage of the huge convenience and advantages experimental results indicate that the proposed method brought by cloud computing and edge computing, and can effectively plan a smooth, collision-free and less can make security decisions on a global perspective. expensive path. Under this framework, a combination of Bayesian SunC01 Room 1 network-based risk assessment and stochastic game IS：Dynamic linearization based data-driven control model-based security decision-making is proposed to 15:50-17:50 generate an optimal defense strategy to minimize Chair: Ronghu Chi Qingdao Univ. of Sci. & Tech. system losses. This method trains models in the clouds CO-Chair: Wenlong Yao Qingdao Univ. of Sci. & Tech. and infers at the edge computing nodes to achieve rapid Beijing Institute of Tech. defense strategy generation. Finally, a case study on the hardware-in-the-loop simulation platform proves the 15:50-16:10 SunC01-1 feasibility of the approach. Data-driven adaptive sliding mode controller design for 13:30-15:30 SunB07-29 nonlinear systems with prescribed performance Dong Liu Shenyang Aerospace Univ. A Fractional Order Medium-Speed Maglev Train Speed Control Method Based on Particle Swarm Optimization Bowen Cao Beijing Jiaotong Univ. This paper addresses the problem of prescribed Yaping Gao Beijing Jiaotong Univ. performance control for nonlinear discrete-time systems Kuanxin Li CRRC Tangshan Co., Ltd. such that the tracking error is constrained to a Jinpeng Zhang Luoyang Optoelectro Tech. predesigned region all the time. Moreover, a new second Development Center order sliding mode control method together with an Zhenyu Zhang Guizhou Univ. of Commerce novel transformed error strategy is proposed to Wenjing Zhang Beijing Jiaotong Univ. guarantee the prescribed convergence rate and steady state error behavior to a predefined region all the time. In this paper, to improve the control accuracy and Further, the designed controller depends only on the robustness of medium-speed maglev trains, a speed input/output data, which is more effective in the complex control methodology based on fractional order PID industrial processes. The potential of the results is (FOPID) tuned through particle swarm optimization illustrated on the simulation example. (PSO) algorithm is proposed. According to the proposed methodology, the coefficients of train aerodynamic 16:10-16:30 SunC01-2 resistance are identified via PSO algorithm. A FOPID Intelligent Sweeper Path Tracking Control Based on speed controller whose coefficients are tuned via PSO Optimal Iterative Learning algorithm is designed to track the desired speed Wenlong Yao Qingdao Univ. of Sci. & Tech. trajectory and reduce the effects of various resistances Beijing Institute of Tech. in the train operation process. To demonstrate the Zhen Pang Qingdao Univ. of Sci. & Tech. effectiveness of the proposed FOPID speed controller, Roughu Chi Qingdao Univ. of Sci. & Tech. some simulations are conducted. The simulation results Wei Shao Qingdao Univ. of Sci. & Tech. show that the proposed speed control algorithm can Dejing Yang Shandong JinKaiFeng Machinery Tech. effectively improve the speed control performance. Co., Ltd.

13:30-15:30 SunB07-30 This paper studies the path tracking control system of intelligent sweeper. According to the fact that the RRT Based Obstacle Avoidance Path Planning for 6-DOF intelligent sweeper mainly carries out the cleaning and Manipulator sprinkling work in a fixed section periodically and has Ben Han Zhejiang Univ. strong repeatability, this paper studies the path tracking Shan Liu Zhejiang Univ. control problem of the intelligent sweeper based on the optimal iterative learning control method. Firstly, the Obstacle avoidance path planning is an important traditional intelligent sweeper path tracking system is research topic in robot operation. As a complex system transformed into the pre-sighting deviation angle with multiple inputs and multiple outputs, highly tracking system. And the dynamic linearization data nonlinear and strong coupling, the manipulator cannot model is obtained by using the improved iterative be directly regarded as a particle in Cartesian space, so dynamic linearization method. Secondly, an iterative many path planning algorithms for mobile robots cannot expansion observer is added in this paper to deal with be directly extended to manipulators. In this issue, uncertain disturbances such as turbulence and based on the rapidly-exploring random tree algorithm, measurement error in the path tracking control system this paper proposed an improved path planning method. of intelligent sweeper. It compensates for the unknown The path is searched in the joint space of the

DDCLS’20 disturbance. Finally, the parameter iteration update rate a novel time-varying data-driven sliding surface is and the optimal learning control rate of the intelligent designed for position subsystem and attitude sweeper control system are proposed. The optimal subsystem, thereby contributing to strong robustness in iterative control method with an iterative expansion terms of disturbances and unmodeled dynamics; 3)The observer can effectively utilize the repeated information feasibility of the proposed HRPIDC strategy is verified by in the path tracking process of the intelligent sweeper simulation studies in comparison with the conventional and effectively estimate the unknown disturbance. And it PID control, and the effectiveness and superiority of the only utilizes the input and output information of the HRPIDC approach when applying to the trajectory system, avoiding the difficult problem of modeling. tracking control of the quadrotor UAVs are further Simulation results show the effectiveness of the demonstrated. proposed method. 17:10-17:30 SunC01-5 16:30-16:50 SunC01-3 Iterative Learning Reliable Control Strategy for a Class Data-Driven Adaptive PID Control of Unknown Quadrotor of SISO Systems with Time-Delay and Actuator Faults UAVs Heng Liu Qingdao Univ. of Sci. & Tech. Dong Nan Dalian Maritime Univ. Yanjie Wang Qingdao Univ. of Sci. & Tech. Jiapeng Li Dalian Maritime Univ. Ruikun Zhang Qingdao Univ. of Sci. & Tech. Yongpeng Weng Dalian Maritime Univ. Lian Lian Shenyang Univ. of Chemical Tech. This manuscript presents an iterative learning control Cunqian Yu Dalian Maritime Univ. strategy for a class of single-input single-output (SISO) Shaowu Li Hubei Univ. for Nationalities nonlinearly parameterized systems with unknown time-varying state delays and actuator faults. Based on In this paper, a novel data-driven adaptive PID control some basic assumptions and the property of the state (DAPIDC) scheme is developed for unknown quadrotor delays and actuator faults of the SISO nonlinear system, UAVs. Main contributions are as follows: 1) By applying we design the P-type iterative learning reliable controller the virtual control strategy and arithmatic inverter to deal with the nonlinearity caused by the time-delays technique, discrete incremental PID control laws are first term and actuator faults. And then, a composite energy designed for position subsystem and attitude function (CEF) is used to show the convergence subsystem; 2) On this basis, an efficient coefficient property of the state tracking error. Finally, a numerical adjustment scheme based on data-driven strategy is simulation is used to verify the correctness and proposed, which can adjust PID controller parameters effectiveness of control scheme. online, thereby contributing to strong adaptability to the unknown dynamics, uncertainties and disturbances; 3) 17:30-17:50 SunC01-6 Finally, the simulation studies are carried out and Iterative Learning Control for Multiple Time-Delays compared with the conventional PID method, so that the Discrete Systems in Finite Frequency Domain effectiveness and superiority of designed DAPIDC Xiaohui Li Jiangnan Univ. scheme is demonstrated. Jianqiang Shen Jiangnan Univ. Hongfeng Tao Jiangnan Univ. 16:50-17:10 SunC01-4 Shoulin Hao Jiangnan Univ. Hybrid Robust PID Control of Unknown Quadrotor UAVs Lichuan Liu Dalian Maritime Univ. This paper developes iterative learning control scheme Dong Nan Dalian Maritime Univ. and the stability conditions for multiple time-delays Jiapeng Li Dalian Maritime Univ. discrete system. By formulating the problem over Yongpeng Weng Dalian Maritime Univ. repetitive process form using 2D theory, sufficient Lian Lian Shenyang Univ. of Chemical Tech. stability conditions for multiple timedelays discrete Shaowu Li Hubei Univ. for Nationalities system are developed along the trial, which guarantees Cunqian Yu Dalian Maritime Univ. the trial-to-trial error monotonic convergence. Moreover, the generalized Kalman-Yakubovich-Popov (KYP) lemma In this paper, a novel hybrid robust PID control (HRPIDC) allows the iterative learning control scheme to develope scheme is proposed to address asymptotical trajectory stability conditions with LMI constraints and analyze in tracking control problem of unmanned aerial vehicles the finite frequency domain. A numerical simulation for (UAVs) with unknown dynamics and disturbances. Main multiple time-delays discrete system is given to verify contributions of this study are as follows: 1) Different the proposed method. from the previous approaches, within the HRPIDC SunC02 Room 2 scheme, discrete incremental PID control laws and IS：ADP and RL for optimal control 15:50-17:50 data-driven sliding mode control (DSMC) are effectively Chair: Ruizhuo Song Univ. of Sci. & Tech. Beijing cohered to deal with the tracking control problems of the CO-Chair: Jinhai Liu Northeastern Univ. quadrotor UAVs; 2) By applying the data-driven strategy,

15:50-16:05 SunC02-1 Data-Driven Nearly Optimal Control for Constrained Adaptive Dynamic Programming Method for Optimal Nonlinear Systems Battery Management of Battery Electric Vehicle Xiong Yang Tianjin Univ. Jiguang Xue Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd. This article develops a novel data-driven policy iteration Chunsheng Yan LiaoNing Electric Power Development (PI) to obtain nearly optimal control of nonlinear systems Stock Co., Ltd. with asymmetric input constraints. The data-driven PI is Dan Wang Electric Power Research Institute of State derived from an early established model-based PI. Grid Liaoning Electric Power Co., Ltd. Owing to the data driven PI sharing the same solution as Jun Wang Electric Power Research Institute of State the model-based PI, the convergence of the data-driven Grid Liaoning Electric Power Co., Ltd. PI algorithm is guaranteed. The implementation of the Jun Wu Electric Power Research Institute of State newly developed data-driven PI algorithm relies on an Grid Liaoning Electric Power Co., Ltd. actor-critic structure consisting of two kinds of neural Zehua Liao Chinese Academy of Sci. networks (NNs). Specifically, the critic NN aims at estimating the value function and the actor NNs aim at One of the main influencing factors of battery electric approximating the control policies. The weight vehicle (BEV) application is the high-cost of the battery. parameters used in the critic and actor NNs are We consider to apply the battery of BEV to smart determined via the least squares method together with residential environments when the BEV is idle, so that the Monte Carlo integration technique. Finally, a we can lower the utility cost. Therefore, an adaptive nonlinear plant is provided to validate the proposed dynamic programming (ADP) method is designed to data-driven PI algorithm. solve the optimal battery management, which avoids the dimension disaster of the complex nonlinear BEV 16:35-16:50 SunC02-4 system. First, the operation modes of the battery are Online Optimal Event-triggered Tracking Control with analyzed, and the problem statement is carried out. Actuator Saturation via ADP Then, the corresponding self-learning optimization Lu Liu Univ. of Sci. & Tech. Beijing algorithm is developed based on ADP. Finally, numerical Ruizhuo Song Univ. of Sci. & Tech. Beijing results by experiment simulations are used to verify the Yi Ren China communications construction ADP algorithm. intelligence transportation Co., Ltd. Yong Zhao Univ. of Sci. & Tech. Beijing 16:05-16:20 SunC02-2 Biao Song Univ. of Sci. & Tech. Beijing ADHDP-Based Housing Energy Management For Two Housing Units With Mobile Storage This paper comes up with an optimal tracking control Jiguang Xue Electric Power Research Institute of State mechanism with saturation controller for some Grid Liaoning Electric Power Co., Ltd. continuous nonlinear systems based on the Chunsheng Yan LiaoNing Electric Power Development event-triggered (ET) mechanism via adaptive dynamic Stock Co., Ltd. programming (ADP). To begin with, the constrained Lu Liu Electric Power Research Institute of State tracking control problems are able to be transformed Grid Liaoning Electric Power Co., Ltd. into common event-triggered optimal control through Jiahan Wang Electric Power Research Institute of State designing an appropriate cost function. Next, a single Grid Liaoning Electric Power Co., Ltd. critic network is to estimate the cost function. Moreover, Xuliang Zhao Electric Power Research Institute of State taking the appropriate triggering condition as the Grid Liaoning Electric Power Co., Ltd. prerequisite, the stability proof of the system is proved Xin Wang Chinese Academy of Sci. according to Lyapunov theory. In addition, the simulations verify the excellent performance of ET-ADP In this paper, a new optimal learning control scheme for algorithm because the optimal control law is only discrete-time nonlinear systems using iterative adaptive updated at the required times influenced by the devised dynamic programming (ADP) approach is developed to triggering condition, which can accomplish high obtain the optimal control law for two-house energy efficiency of resources and energy consumption of the systems. First, the operation modes of the battery are system. analyzed, and the problem statement is carried out. Next, according to the data of the users, action dependent 16:50-17:05 SunC02-5 heuristic dynamic programming (ADHDP) is developed Application of NARX dynamic neural network in blood to obtain the optimal control law. Numerical results are glucose prediction model given to illustrate the performance of the present Menglin Xu Univ. of Sci. & Tech. Beijing method. Ruizhuo Song Univ. of Sci. & Tech. Beijing Yong Zhao Univ. of Sci. & Tech. Beijing 16:20-16:35 SunC02-3 Biao Song Univ. of Sci. & Tech. Beijing

DDCLS’20

Juan Tang Ericsson (China) Commodity Co., Ltd. on conditional autoencoder (CVAE) and generative adversarial networks (GAN) is proposed. This method Establishing a blood glucose prediction model and combines the advantages of CVAE and GAN, and controlling the amount of insulin injected according to generates high-quality samples steadily. The proposed the predicted blood glucose value can effectively reduce CVAE-GAN method can not only reconstruct the missing the harm caused by high and low blood glucose to the MFL samples, but also generate a large amount of real human body. Because the dynamic process of and diverse defect sample, which solves the problem of glucose-insulin metabolism in human body is a typical low accuracy of the defect detection model due to non-steady-state and non-linear process, and has a large insufficient samples and lack of diversity of samples. time-lag dynamic characteristic, it is difficult for linear The defect sample are collected from the domestic models to accurately describe the blood glucose-insulin in-service oil pipelines in experiments. The experimental model. With the development of artificial neural network results illustrate that the proposed method can and its application in all aspects of modern times, this effectively generate high-quality samples. paper proposes to use NARX dynamic neural network to build blood glucose-insulin model, and use the data 17:35-17:50 SunC02-8 generated by UVa/Padova simulation platform for Short-Term Wind Speed and Wind Power Prediction network training. Finally, through MATLAB simulation Based on Meteorological Model Modification verification, by calculating the mean square error and Tianze Liu Shenyang Institute of Engineering Clark error grid analysis method, it is shown that the Yan Zhao Shenyang Institute of Engineering blood glucose can be effectively predicted. Gang Sun State Grid Liaoning Electric Power Supply Co., Ltd. 17:05-17:20 SunC02-6 Yanjuan Ma Shenyang Institute of Engineering Distributed Optimal Coordination Control for Continuous-Time Nonlinear Multi-Agent Systems With Accurate prediction of wind speed and wind power is of Input Constraints great significance to the operation, planning, Yunhong Deng Univ. of Chinese Academy of Sci. dispatching and control of power system. In order to Jun Xiao Univ. of Chinese Academy of Sci. make full use of the effective information provided by Qinglai Wei Univ. of Chinese Academy of Sci. SCADA system and NWP to further improve the prediction accuracy of wind speed and wind power. A This paper is concerned with an optimal coordination short-term wind speed and wind power prediction control problem for nonlinear multi-agent systems method based on meteorological correction model is (MASs) with constraints of the control inputs. The idea of proposed in this paper. Firstly, the meteorological model daptive dynamic programming (ADP) algorithm is to use based on matrix completion algorithm is established to the policy iteration to solve the coupled Hamilton-Jacobi modify the meteorological data. Secondly, the network is equations. First, a suitable non-quadratic functional is trained with the data of meteorological model introduced into the cost function to transform the modification as input and the actual power of fan as question into an optimization problem. Second, a output, and the prediction model based on LSTM distributed control law is designed for each agent, which network is established. Finally, the short-term prediction aims that the cost function of the MASs converge to of wind speed and wind power is completed. The Nash equilibrium. Next, the analysis of the convergence measured data from a wind farm is used for verification. is indicated that the iterative cost functions of nonlinear The research results show that the information in multi-agent systems is convergent. Neural network multiple data sources can be well used in the proposed (NNs) are used to approximate the cost functions for the method to complete the prediction of wind speed and calculation of the control laws. Finally, simulation results wind power. And in the future, the waste of wind show the effectiveness of the coordination control resources can be effectively reduced, so as to realize the algorithm. economic and stable operation of the power grid. SunC03 Room 3 17:20-17:35 SunC02-7 Data-driven fault diagnosis and health maintenance (IV) A Data Reconstruction Method based on Adversarial Conditional Variational Autoencoder 15:50-17:50 Yifu Ren Northeastern Univ. Chair: Yandong Hou Henan Univ. Jinhai Liu Northeastern Univ. CO-Chair: Heqing Sun ABB Engineering (Shanghai) Ltd. Jianan Zhang Northeastern Univ. Lin Jiang Northeastern Univ. 15:50-16:10 SunC03-1 Yanhong Luo Northeastern Univ. A Deep Learning Method for Rolling Bearing Fault Diagnosis through Heterogeneous Data Aiming at the problem of sample missing for magnetic Wei Zhou Henan Univ. flux leakage (MFL), a data reconstruction method based Yandong Hou Henan Univ.

Vibration signals of rolling bearing have multiple monitoring methods may show poor performance for the heterogeneous forms. Traditional fault diagnosis time-varying processes since they fail to track the methods use 1D time-series signals or converted 2D time-varying characteristics. As Gaussian Mixture Model signals for fault diagnosis. However, using the former (GMM) has been widely used for process monitoring, will lose the spatial neighborhood features; using the this paper presents a new incremental GMM model for latter will ignore time-series features, which caused monitoring time-varying processes. First, an incremental information waste. In this paper, a new heterogeneous GMM (IGMM) model is proposed, which can recursively form of bearing vibration signals is proposed to address update model parameters, adaptively add new Gaussian the problem. Our contributions of include: First, we components and discard the irrelevant component proposed dynamic waveform sequences, which is a new based on the shifting samples online. Then the Bayesian heterogeneous form and can simultaneously reflect Inference Probability (BIP) is introduced for monitoring time-series features and spatial neighborhood features statistics and a two-level partition strategy that can in vibration signals. Second, the CCLSTM separate normal shifting samples from fault samples is (Conv-ConvLSTM) model is designed to extract the proposed, which reduces the possibility of adding fault above two features layer by layer. Relying on the samples to the model. On the basis of IGMM model, an powerful feature extraction capability of CCLSTM, it is adaptive monitoring scheme is developed, which can possible to simultaneously extract the time-series track the time-varying characteristics of processes. features and spatial neighborhood features in a single Finally, a time-varying numerical example and the fault diagnosis network. The experimental verification Tennessee Eastman process are adopted to validate the through real bearing fault data sets shows that this feasibility of the proposed monitoring model. method can effectively improve the diagnostic accuracy. Experimental results clearly demonstrate the adaptiveness of the monitoring model to time-varying 16:10-16:30 SunC03-2 processes and the ability to avoid false updates. Fault Diagnosis Using Neural Networks for Parallel Shaft Gearboxes and Discussion on Its Generalization Ability 16:50-17:10 SunC03-4 Zhanchi Liu ABB Engineering (Shanghai) Ltd. A Novel Local Selective Ensemble-based AdaBoost Heqing Sun ABB Engineering (Shanghai) Ltd. Method for Fault Detection of Industrial Process Yuan Xu Beijing Univ. of Chemical Tech. This paper presents a diagnosis method using back Cuicui Zhang Beijing Univ. of Chemical Tech. propagation neural networks (BPNNs) for parallel shaft Qunxiong Zhu Beijing Univ. of Chemical Tech. gearboxes and discussion on its generalization ability. Yanlin He Beijing Univ. of Chemical Tech. Feature vector, which is input of the BPNN, is extracted from the spectrum combining the data model. For the sake of guaranteeing the security of complex Normalization method is proposed based on the industrial system, it is important to accurately and diagnosis mechanics. BPNN models are developed efficiently detect the faults. AdaBoost algorithm is an using the collecting data including two healthy and four effective fault detection method. It can generate a large faulty states of nine different speed and loading number of weak classifiers in iterations and combine conditions. The performance of the trained models is many of these weak classifiers into the strong classifier evaluated using the traditional method by setting to solve the classification problem for fault detection. training and testing sets. At the same time, the For the traditional AdaBoost, several of these poor weak generalization ability is evaluated using blind data set. classifiers are often ignored and not fully used. However, The results reveal that the diagnosis accuracy is high in the weak classifiers with poor performance may store the training and testing sets. However, for the data of the significant information and pay more attention to the new working condition, the diagnosis accuracy will be difficult samples. To solve these problems, we propose a obviously lower, which constrains the application in real local selective ensemble-based AdaBoost practice. Some suggestions are made to improve the (AdaBoost-LSE) in this article. Firstly, error feedback generalization ability of the data-driven diagnosis ELM (EFELM) is introduced to establish the basic weak methods. classifier. Through the iteration of AdaBoost, these weak classifiers based on EFELM are generated. Secondly, 16:30-16:50 SunC03-3 these weak classifiers are divided into good weak Incremental Gaussian Mixture Model for Time-varying classifiers and bad weak classifiers based on the Process Monitoring classification accuracy. The poor weak classifiers with Qingyang Dai Zhejiang Univ. good performance are selected by calculating the Chunhui Zhao Zhejiang Univ. classification accuracy for the targeted samples. Thirdly, the strong classifier of AdaBoost-LSE is constructed by With the increasing complexity of industrial production, integrating the original good weak classifiers and some data-driven based monitoring methods attract more of these poor weak classifiers with good performance. attention. However, the conventional static process To verify the efficiency of AdaBoost-LSE, the Tennessee Eastman (TE) simulation process is used. The 70

DDCLS’20 experimental results reveal that the proposed traditional multi-class SVDD. AdaBoost-LSE can greatly improve the accuracy of fault SunC04 Room 4 detection. Statistical learning and machine learning in automation field (II) 15:50-17:50 17:10-17:30 SunC03-5 Chair: Chunhui Zhao Zhejiang Univ. 基于的多工况涡扇发动机剩余寿命预测 Shapelets CO-Chair: Xiaofeng Yuan Central South Univ. 丁恺林北京信息科技大学马洁北京信息科技大学 15:50-16:10 SunC04-1 吴锐北京信息科技大学 Attention M-net for Automatic Pixel-Level Micro-crack 在工业领域的许多实际应用中，准确预测某个系统在未来时刻 Detection of Photovoltaic Module Cells in 发生失效的具体时刻至关重要，它不仅能够保障操作者的人身 Electroluminescence Images 安全，还能减少系统运行中的维护周期、维护成本。对于一个 Yu Jiang Zhejiang Univ. 具体的系统、子系统或组件而言，采用退化过程的传感器数据 Chunhui Zhao Zhejiang Univ. 估计的剩余寿命(Remaining Useful Life, RUL)的方法，是其 Waner Ding Zhejiang Energy Group R & D 运行失效时间判定的一种有效方法。针对非线性、工况变化等 Ling Hong Zhejiang Energy Group R & D 复杂的退化过程，提出了如下方法完成 RUL 预测算法：使用 Qu Shen Zhejiang Energy Group R & D 恰当的预处理方法，采用长短期记忆 (Long Short-Term Memory, LSTM)-自动编码器(Auto Encoder, AE)和健康指数 In the solar power industry, quality inspection of solar (Health Index, HI)模型建立 HI，利用时间序列 shapelets 方 cells is a very important part of the production and 法完成工况分类，多层 LSTM 神经网络对时间序列进行预测， application process. Micro-crack is a type of common 综合实现了 RUL 预测、预估设备的失效时刻，并采用航空涡 defect that may be present in photovoltaic (PV) module 扇发动机引擎仿真数据集验证了方法的有效性。 cells which can reduce the power generation efficiency and service lifetime. However, it is difficult to identify 17:30-17:50 SunC03-6 micro-crack by the naked eye because it is readily A Data-Driven Operating Performance Assessment confused with non-uniform backgrounds and complex Method based on Weighted Multi-Sphere Support Vector textures in Electroluminescence (EL) images. In this Data Description paper, we propose Attention M-net which combines Chuanfang Zhang Univ. of Sci. & Tech. Beijing efficient segmentation model structure and attention Kaixiang Peng Univ. of Sci. & Tech. Beijing mechanism. It is a novel micro-crack detection model for Jie Dong Univ. of Sci. & Tech. Beijing automated pixel-level micro-crack detection of PV module cells. The M-shaped structure solves “All Black” In modern hot rolling process, operating performance issue that is easy to occur due to the severe imbalance assessment is of great practical significance for guiding of the micro-crack segmentation dataset. And the production adjustment for operators. From the integration of attention mechanism into the network perspective of classification, operating performance significantly improves the accuracy of segmentation. assessment is a multi-class classification problem. Because of the above two advantages, the proposed Since support vector data description (SVDD) is a model can be accurately learned from a small annotated one-class classifier, conventional methods usually dataset, thereby saving the time of micro-crack EL construct an independent SVDD model for each class, images collection and pixel-level annotations. We which ignores the correlation among different classes. extensively trained the proposed model on a small The hyperspheres of different classes may not be dataset of 20 annotated EL images which include 10 isolated but overlapped. If a test sample exists in the monocrystalline and 10 polycrystalline. The evaluated overlapping region, how to determine which class it results on the test dataset demonstrate the high belongs to is a knotty problem. Moreover, conventional efficiency and accuracy of Attention M-net for pixel-level methods treat all samples equally, but in practice, the micro-crack detection of PV module cells in EL images. sample number of different classes can be imbalanced, which will affect the classification performance of SVDD. 16:10-16:30 SunC04-2 In this study, an operating performance assessment Synchronization Control of Position and Velocity for method based on weighted multi-sphere SVDD Heterogeneous Teleoperation System (WMSVDD) is proposed for solving the aforementioned Xia Liu Xihua Univ. issues. WMSVDD considers the interactions among Chengwei Pan Univ. of Electronic Sci. & Tech. of China different classes in a unified way, optimizes the Yong Chen Univ. of Electronic Sci. & Tech. of China hyperspheres of different classes globally, and introduces a weight coefficient to the model for Due to different mechanical structures, communication eliminating the affects of uneven class sizes. Simulation methods and task spaces, the master robot and the results on a real hot rolling process illustrate the slave robot in a heterogeneous teleoperation system are effectiveness of the proposed method comparing to the 71 difficult to keep synchronization. This paper presents a results generated by Bagging-ELM model and those synchronization control strategy of position and velocity generated by BP model, RBF model, ELM model. for the heterogeneous teleoperation system. The Compared with other models, the Bagging-ELM can interaction force between the operator and the master is obtain higher accuracy and stability. utilized to make the master generate the position information and position variation. The position 17:10-17:30 SunC04-5 information of the master is used to control the position Evaluation and Analysis of Comprehensive Influence of of the slave. Meanwhile, the position variation of the Papers: Multidisciplinary as an Example master is employed to indirectly control the velocity of Yangyang Jiang Beihang Univ. the slave. In this way, the position and velocity of the Bo Jin China Academy of Electronics & slave can be in synchronization with those of the master. Information Tech. The experimental results demonstrate that with the proposed control strategy the slave can act on the target By introducing supplementary evaluation indexes, this more accurately and quickly, enhancing the operator's paper makes up for the deficiencies of lag, injustice, operational flexibility. discipline bias, and one-sidedness of traditional citation evaluation. Multidisciplinary papers are selected as the 16:30-16:50 SunC04-3 data source. Correlation analysis, validity analysis, Co-occurrence Mining of “data driven control”, “model factor analysis, and principal component analysis are free adaptive control” and “iterative learning control” in used to analyze the data of each index to construct a Chinese Dissertation Data comprehensive influence evaluation model. The results Xiangpeng Liu Qingdao Univ. of Sci. & Tech. show that the model is a comprehensive evaluation Yang Ai Qingdao Univ. of Sci. & Tech. model with academic evaluation as the main and social Lantian Guo Qingdao Univ. of Sci. & Tech. evaluation as the auxiliary. The comprehensive influence Ronghu Chi Qingdao Univ. of Sci. & Tech. score of papers can be calculated through comprehensive influence formulas, to obtain a more This paper focuses on the master and doctoral comprehensive and reasonable evaluation result. This dissertations on "data-driven control", "model-free paper provides data support for the proportion of each adaptive control" and "iterative learning control" index data in the comprehensive evaluation of academic collected by Chinese National Knowledge Infrastructure papers, and also provides a reference for index selection (CNKI) database before October 31, 2019. Exploiting big and evaluation model optimization of comprehensive data technology, three topics mentioned above are influence evaluation of papers. studied in the form of mapping knowledge graph by Python, ROSTCM and Ucinet social network analysis 17:30-17:50 SunC04-6 software. Our research mainly explores and analyzes the A Time Window based Two-Dimensional PCA for temporal evolution as well as development status of the Process Monitoring and Its Application to Tennessee subject words, the cultivation organizations, the fund Eastman Process support, the co-occurrence of key words, the Xiaofeng Yuan Central South Univ. co-occurrence of dissertations topics and the Di Wang Central South Univ. characteristics of supervisor -student relationship, etc., Yalin Wang Central South Univ. which can provide references for relevant research Weiming Shao China Univ. of Petroleum fields. Multivariate statistical analysis methods like PCA have 16:50-17:10 SunC04-4 been widely utilized for fault diagnosis and quality Bagging-ELM Model for Heating Furnace Thermal control. Nevertheless, the traditional PCA based Efficiency Prediction methods have some limitations in dealing with the Kai Shang Shengli College China Univ. of Petroleum dynamic data information which extensively exists in Xianglong Zeng China Huanqiu Contracting & Engineering modern process industry. This paper developed a time (Beijing) Co. window based 2D-PCA model to strengthen the Xiaorui Dong Shengli College China Univ. of Petroleum capability of extracting dynamic features for process monitoring. First, this model uses the time window to Thermal efficiency is a very important index for heating construct 2-dimensional data matrices piece by piece to furnace in chemical industry. In order to predict the keep as much dynamic information as possible between complicated variables, the Bagging-ELM network based samples. Second, 2D-PCA is applied to these on ensemble learning and Extreme Learning Machine is two-dimensional data samples for dynamic feature adopted. The proposed model is applied to predicting learning. Finally, general statistics are calculated for the net hourly electrical energy output in Combined online monitoring to detect abnormal states. Finally, Cycle Power Plant data set and the thermal efficiency of Tennessee Eastman (TE) process is used to verify the the heating furnace. There is a comparison between the effectiveness of the developed 2D-PCA monitoring

DDCLS’20 strategy. obtain the faster response and better anti-disturbance capability than traditional asymptotic convergence SunC05 Room 5 control system. Further, the signs of the control gains IS ： Distributed learning and control of networked which are called control directions that are need to be systems (II) 15:50-17:50 known in advance. However, in some practical Chair: Lin Zhao situations, this condition is usually not satisfied, for CO-Chair: Juntao Li example the unchecked visual servo system, autopilot uncertain ships and unmanned sailboats heading control 15:50-16:10 SunC05-1 system where the control directions are often unknown, Closed-Loop Pitch Attitude Control of Biomimetic which increases the difficulty of control system design Robotic Fish and the risk of instability in control system. So the Yujie Zhang Lanzhou Jiaotong Univ. Nussbaum function is introduced to assist the controller Zonggang Li Lanzhou Jiaotong Univ. design and to avoid the serious problems caused by Yajiang Du Lanzhou Jiaotong Univ. unknown control directions.

This paper considers the pitch control of a robotic fish Considering the above discussion, this paper put with three degree-of-freedom (DOF) pectoral fins, forward an adaptive finite-time command filtered wire-driven flexible body and a passive caudal fin. The backstepping control strategy. The finite-time command linear model with undetermined parameters is first filter which can filter the virtual control signals to get suggested to describe the dynamics of the pitch angles, intermediate control signals, then the explosion of which are the control parameters to adjust the posture of complexity problem will be overcome well. At the same robotic fish. Then, a control network based on Central time, the states of multi-agents can fast track the output Pattern Generators (CPGs) is proposed to separately of the leader in finite time. Then in order to eliminate the adjust the state of each DOF. Finally, a compound filter error caused by finite-time command filter, the error controller is designed to adjust the amplitudes of compensation signal is constructed which can ensure pectoral Fins such that the desired pitch angle is the better tracking accuracy and transient performance. tracked, in which an adaptive law combined with RBF Based on the adaptive fuzzy logical system, the neural network is proposed to estimate the unknown dynamic is approximated accurately and only undetermined parameters. The stability of the proposed one parameter is need in the adaptive control law. It is algorithm is proved and then the validity is investigated worth noting that this paper studies non-strict feedback by the simulation results. multi-agent systems, where the state variables problem in the non-strict feedback system is well solved through 16:10-16:30 SunC05-2 the scaling of inequalities based on the basis function Fuzzy Adaptive Finite-time Consensus Tracking for vector of fuzzy logical system. Under the directed graph Nonstrict Feedback Nonlinear Multi-Agent Systems theory, this paper uses Lyapunov stability theory to Guoqing Liu Qingdao Univ. proof the effectiveness of the presented method and we Lin Zhao Qingdao Univ. can obtain that the consensus tracking error of the closed-loop system can converge to a sufficiently small In recent years, the problem of consensus in distributed neighborhood of the origin and all signals are bounded control about multi-agent systems has got extensive in finite time. attention due to its wide application in engineering, such as multiple unmanned boats, multiple aircrafts, multiple 16:30-16:50 SunC05-3 sensor network and so on. For multi-agent systems, the Solution Path Algorithm for Twin Multi-class Support complex nonlinearities and system uncertainties often Vector Machine exist in it, which will bring serious challenges for the Liuyuan Chen Henan Normal Univ. controllers design. The traditional backstepping control Kanglei Zhou Henan Normal Univ. method is usually adopted to solve the problem of Junchang Jing Henan Univ. of Sci. & Tech. nonlinear systems, but this method may be invalid for Haiju Fan Henan Normal Univ. simplifying controller design owing to the existence of Juntao Li Henan Normal Univ. explosion of complexity in the backstepping process. It is necessary to develop efficient control methods for Twin support vector machines (TSVM) are widely used in high-order nonlinear and uncertain multi-agent systems. machine learning because of its superior computing performance and good generalization ability. However, As for the traditional multi-agent controller design TSVM also faces problems such as parameter tuning scheme with asymptotic convergence, it is unable to and how to quickly solve the multi-classification guarantee the finite time convergence rate of the control problem. To solve these problems, the piecewise linear systems and that will be limited in practical applications. theory is proved using linear equations and block matrix It is worth emphasizing that the finite-time control can theory in this paper, which lays a theoretical foundation

73 for the rapid solution path algorithm of the problem. The loss. For data imbalance in intrusion detection, we proposed algorithm in this paper will reduce the improve the risk estimator of nnPU through focal computational cost greatly. Experimental results show loss(FL-nnPU). The dynamic weights in focal loss is that the optimal parameters selected by the algorithm used to balance the small class prior. The experiments have higher prediction accuracy due to the enlargement result show that FL-nnPU have a close performance to of the understanding space. binary classification, and it performs better than nnPU under data imbalance problems. 16:50-17:10 SunC05-4 Distributed Consensus of Layered Multi-agent Systems 17:30-17:50 SunC05-6 Subject to Attacks on Edges Function Projective Synchronization of Neutral Complex Guanghui Wen Southeast Univ. Dynamic Network with Unknown Time-varying Coupling Strength Ying Yang Xidian Univ. This paper addresses the node-to-node consensus Junmin Li Xidian Univ. problem for two-layered MASs (multi-agent systems) subject to attacks on communication edges. Unlike most This paper addresses the function projective existing two-layered MAS models, the considered MASs synchronization control for a class of neutral complex are allowed to have heterogeneous inner communication dynamic network with nonlinear coupling strength and topologies between the different layers. By using the unknown time-varying parameters. An adaptive linear transformation technique, some sufficient criteria controller is designed for neutral complex network with are first given to achieve consensus among the leaders time-varying coupling strength. In addition, a sufficient within the leader layer where the condition that the condition is given to guarantee the function projective interaction topology has directed spanning trees with a synchronization of complex dynamic networks. The fixed root required in most existing work has been simulation example demonstrates the proposed method. removed. Furthermore, based on the relative outputs, observer-based controllers are designed to achieve SunC06 Room 6 node-to-node consensus among the two layers. By IS：New trends in data driven and Industrial AI control developing a new kind of multiple Lyapunov function 15:50-17:50 (MLF) based on a bisection search method, some criteria Chair: Dezhi Xu Jiangnan Univ. are established under which the node-to-node CO-Chair: Sen Chen Shaanxi Normal Univ. consensus error will converge into a bounded set asymptotically. It is interestingly found that a less 15:50-16:05 SunC06-1 conservative average dwell time (ADT) constraint is Nonsingular Terminal Sliding Mode Control for PMLSM obtained for achieving node-to-node consensus by Based on Disturbance Observer utilizing the proposed MLF compared with that yielded Bo Ding Jiangnan Univ. by the traditional MLF constructed from nonsingular M Dezhi Xu Jiangnan Univ. matrix theory. Moreover, we show that consensus can be Weilin Yang Jiangnan Univ. achieved asymptotically in the layered MASs with the Kaitao Bi Jiangnan Univ. same inner topology subject to synchronous attacks Wenxu Yan Jiangnan Univ. under some suitable conditions. In order to achieve high precision speed control of the 17:10-17:30 SunC05-5 permanent magnet linear synchronous motor (PMLSM), Intrusion Detection based on Non-negative this paper proposed a nonsingular terminal sliding mode Positive-unlabeled Learning control (NTSMC) with nonlinear disturbance observer Sicai Lv Harbin Institute of Tech. at Weihai (NDO). First, the model of PMLSM based on field Yang Liu Harbin Institute of Tech. at Weihai oriented control (FOC) is given. Then, a NTSMC is Zhiyao Liu China Industrial Control Systems proposed via backstepping. The controller can make the Cyber Emergency Response Team states reach the manifold in finite time. With the motor Wang Chao Harbin Institute of Tech. at Weihai moving, it will be disturbed by friction and load, which Chenrui Wu Harbin Institute of Tech. at Weihai will affect the system stability. Therefore, a NDO is Bailing Wang Harbin Institute of Tech. at Weihai introduced to estimate the unknown disturbance. The observed value is dynamically compensated to the Due to the diversity of network traffic flow, intrusion NTSMC, which improves the control precision of the detection is usually studied as an anomaly detection PMLSM system. Finally, the proposed algorithm is problem. In this paper, Positive-unlabeled with applied in dSPACE compared with PI, the experimental Non-negative Risk Estimator(nnPU) learning is results show the proposed controller has better dynamic introduced for intrusion detection. The cyber attacks is and static performance. treated as positive samples in PU learning. A risk estimator is raised to estimates the binary classification 16:05-16:20 SunC06-2

DDCLS’20

Fault-Tolerant Control for Load Frequency Control Kaitao Bi Jiangnan Univ. System via a Fault Observer Weilin Yang Jiangnan Univ. Yiwei Zhang Jiangnan Univ. Dezhi Xu Jiangnan Univ. This paper presents a control scheme for command Weilin Yang Jiangnan Univ. filtering and backstepping for DC microgrid systems Kaitao Bi Jiangnan Univ. with hybrid energy storage devices. The proposed Wenxu Yan Jiangnan Univ. control scheme strictly controls the active power output by each power converter unit (PCU), and stabilizes the To guarantee the stability and reliability of the multi-area DC bus voltage, regardless of load transients. In terms of interconnected power system under sensor failures, this power distribution, the energy management system paper presented a fault observer based active sensor performs first-order low-pass filtering according to the fault-tolerant control scheme for load frequency control required compensation power, where the high-frequency in power system. A fault observer is firstly introduced in part is borne by the supercapacitor and the order to estimate the sensor failures in different control low-frequency part is borne by the battery. As to design areas. Then, based on the estimation of the fault, an of the controller, the use of backstepping control easily distributed integral-type sliding mode fault-tolerant lead to differential expansion, which makes the design of control scheme is presented to ensure that the the controller difficult. The command filter is used to frequency of each subsystem in the multi-area solve this problem. Finally, the simulation is set up in interconnected power system can run stably within the Matlab/Simulink environment. The simulation results allowable range. Finally, the simulation experiment by show that the designed power distribution strategy and applying different sensor faults in a three-area controller have excellent control effect and strong interconnected power system was operated, and the robustness. simulation results prove the effectiveness and the desired performance of the presented distributed active 16:50-17:05 SunC06-5 fault-tolerant control scheme. On performance analysis of extended state observer for a class of systems with multiple uncertainties and 16:20-16:35 SunC06-3 biased measurement Adaptive Terminal Sliding Mode Backstepping Control Sen Chen Shaanxi Normal Univ. for Virtual Synchronous Generators Zhiliang Zhao Shaanxi Normal Univ. Yongbao Sun Jiangnan Univ. Wengyan Bai Beijing Aerospace Automatic Control Dezhi Xu Jiangnan Univ. Institute Weilin Yang Jiangnan Univ. Kaitao Bi Jiangnan Univ. This paper studies the state estimation problem of a Wenxu Yan Jiangnan Univ. class of systems with multiple uncertainties and biased measurement. Since the observability of such system is A novel control strategy based on microgrid (MG) is not satisfied，the estimations for states and uncertainties proposed in this paper to improve the low inertia definitely have biases. For the extended state observer characteristics of traditional voltage source three-phase which aims to estimate the system states and the converters (VSC). The proposed control strategy dominant term of uncertainties ， the quantitative consists of the Virtual Synchronous Generator (VSG), estimating performance is studied in the paper. backstepping control and adaptive terminal sliding Furthermore，the biased estimation error is explicitly mode control. The VSG can improve the virtual inertia of shown，which can be derived from the following three the system in the application of inverter, the sliding parts: 1) the biased measurement; 2) the unobservable mode method can improve the robust performance of dominant uncertainty; 3) the non-dominant term of the system, and the adaptive control method can be multiple uncertainties. The theoretical analysis in the used to compensate the parameter transient error of the paper can help practitioners quantitatively evaluate the system. The low inertia characteristic of MG system in effectiveness of the designed extended state observer. traditional VSC is improved. The simulation results has shown that the designed controller has better 17:05-17:20 SunC06-6 anti-interference and inertia performance. Data-Driven Nonlinear Active Disturbance Rejection Longitudinal Tracking Control of Unmanned Vehicles 16:35-16:50 SunC06-4 Zhi-Liang Zhao Shaanxi Normal Univ. Command-Filtered Backstepping Controller for DC Jiqiang Zhang Shaanxi Normal Univ. Microgrid with Hybrid Energy Storage Devices Sen Chen Shaanxi Normal Univ. Wei Zhang Jiangnan Univ. Yan Xia Yangzhou Univ. Longitudinal tracking control of unmanned vehicles is to design feedback controllers to make adjacent vehicles Dezhi Xu Jiangnan Univ. of a group of unmanned motion vehicles keep a 75 prescribed distance. High performance control of Tech. longitudinal tracking meets challenge in the complicated Zhijun Yang State Key Laboratory for Precision motion environment, where the dynamic models of the Electronics Manufacturing Tech. & motion vehicles are difficult to be build. To improve the Equipment longitudinal-tracking control performance without using exact mathematic models, in this presentation, we With the development of electronic information industry, propose a novel data-driven nonlinear active both the precision and speed of motion stage are disturbance rejection longitudinal control design required to be improved more quickly. However, the method for a group of cruising unmanned vehicles. We friction is an important and uncertain factor that restricts neither utilize the exact mathematic model nor the the precision of motion stage. In order to achieve long continuous output for the controller design. The stroke and high precision of the stage, this paper information used for the controller design is only the comprehensively optimize the structure and controller sampled discrete-time signal. We give a proof sketch of parameters of the rigid-flexible coupling motion stage. the stability and convergence of the feedback The nonlinearity and uncertainty parts of the stiffness closed-loop systems. Simulations are carried out to and damping are estimated and compensated by the validate the effectiveness of the proposed control extended state observer (ESO) in the frame of active method. disturbance rejection control (ADRC). Then, the parameters of the mechanical structure and controller 17:20-17:35 SunC06-7 are optimized comprehensively to minimize the Disturbance estimation and attenuation for maximum tracking error. In order to verify the repetitive-control systems with unknown nonlinearity effectiveness of this method, it has been compared with Fuxi Jiang Hunan Univ. of Sci. & Tech. the traditional parameter serial optimal design method in Lan Zhou Hunan Univ. of Sci. & Tech. simulation. The results show that this method can Zhu Zhang Hunan Univ. of Sci. & Tech. improve the performance of the rigid-flexible coupling Zhuang Jiang Hunan Univ. of Sci. & Tech. motion stage, and thereof providing an efficient optimal design method for such a stage. Changchao Liao Hunan Univ. of Sci. & Tech. SunC07 Poster Hall This paper presents a framework for a class of Interactive session (II) 15:30-17:30 single-input-single-output (SISO) nonlinear systems with Chair: Chaofang Hu Tianjin Univ. periodic tasks and subject to aperiodic disturbances CO-Chair: Meng Zhou North China Univ. of Tech. using linear-extended-state-observer (LESO)-based repetitive control method. A two-stage design procedure 15:50-17:50 SunC07-1 is devised to enhance the disturbance estimation and Improved Box-Cox Transformation Based Residual Life attenuation ability of repetitive-control system where Predictions for Bearings design of LESO is separated from design of repetitive Yaohui Ma Huazhong Univ. of Sci. & Tech. control law. An LESO is designed to estimate and Qiuhui Ma Huazhong Univ. of Sci. & Tech. compensate the nonvanishing lumped disturbance in Hong Zhang Huazhong Univ. of Sci. & Tech. real time fashion. The stability criterion of the Yong Zhang Wuhan Univ. of Sci. & Tech. LESO-based repetitive-control system is established in Ying Zheng Huazhong Univ. of Sci. & Tech. the presence of unknown nonlinear dynamics. The simulations demonstrate that the proposed LESO-based Rolling element bearing is one of the most critical repetitive-control system can achieve satisfactory components in rotating machines. Effectively predicting disturbance rejection and tracking control performances the Remaining Useful Life (RUL) of the bearing can simultaneously. Furthermore, a slight correction to the prevent the occurrence of sudden equipment failure. In amount of the delay of the repetitive controller leads to a this paper, the time-domain features of bearing vibration substantial decrease in steady-state tracking error. signals are extracted and then these features are fused. In order to establish a more accurate prediction model, 17:35-17:50 SunC06-8 the health state of bearing is divided into health stage Optimal Design for Parameters of Structure and and degrad Controller for Rigid-Flexible Coupling Motion Stage stage, the Box-Cox transformation method is applied on Liyun Su State Key Laboratory for Precision the data. The transformation parameter of the sample Electronics Manufacturing Tech. & number curve is further predicted by Support Vector Equipment Regression (SVR). Then, Box-Cox transformation is Ruirui Huang State Key Laboratory for Precision performed using the predicted parameter and a linear Electronics Manufacturing Tech.& degradation model is established to predict the RUL of Equipment the bearing. Finally, the effectiveness of the proposed Hao Peng Guangdong Provincial Key Laboratory for approach is verified with the experimental data on Micro-Nano Manufacturing Equipment bearings’ accelerated life tests provided by FEMTO-ST

DDCLS’20 institute. Results suggest that the strategy can reduce the training time and reconstruction error rate of deep learning 15:50-17:50 SunC07-2 model, and improve the classification accuracy of fault Long-term RUL Prediction of Bearings with Signal diagnosis. Amplitude Regulation and Accumulative Feature Xiaoyu Yang Huazhong Univ. of Sci. & Tech. 15:50-17:50 SunC07-4 Yong Zhang Wuhan Univ. of Sci. & Tech. A Data-Driven Fault Tolerant Control of Singularly Weidong Yang Huazhong Univ. of Sci. & Tech. Perturbed Systems Yanwei Wang Wuhan Institute of Tech. Lei Liu North China Univ. of Tech. Ying Zheng Huazhong Univ. of Sci. & Tech. Shuangbo Li North China Univ. of Tech. Cunwu Han North China Univ. of Tech. Remaining Useful Life (RUL) prediction is essential for Ying Yang Peking Univ. the running bearings. An accurate RUL estimation can help the maintenance decision reliable. The accuracy of A data-driven fault tolerant control for singularly RUL prediction is greatly influenced by the health index perturbed systems is presented in this paper. It is feature. Traditional health index feature suffers from the founded on the data-driven full state observer and Youla long constant process and the vertical change controller parameterization. Based on the multiplex degradation. In this paper, a regulated amplitude signal observer residual generator and the identification of the and an accumulative feature is proposed. The regulated parity subspace, an extended internal model control signal reduces the vertical change degradation, which (EIMC) structure is proposed and then the design of the makes the prediction easily. And the accumulative parameters is given accordingly. Since the system is feature changes the long constant process to an disturbed by faults, the controller proposed in this paper increase process, which makes the prediction possible can accurately track the fault parameters. At last a at the early life time. The Support Vector Regression numerical example is given to illustrate the feasibility (SVR) model is adopt for the direct RUL prediction. In and effectiveness of the method. order to verify the effective of our method, the PRONOSTIA platform is used. The result shows that our 15:50-17:50 SunC07-5 method behavior better than the traditional feature Joint Sparse Principal Component Analysis Based method. Roust Sparse Fault Detection Wenlan Jiang Tsinghua Univ. 15:50-17:50 SunC07-3 Tao Zhang Tsinghua Univ. A Deep Learning Model with Adaptive Learning Rate for Huangang Wang Tsinghua Univ. Fault Diagnosis Xiaodong Zhai Tongji Univ. In this paper, a novel variant of PCA, joint sparse Fei Qiao Tongji Univ. principal component analysis (JSPCA), is adopted into

robust sparse fault detection. By imposing l 2,1 norm With the increasing amount of data in the field of jointly on the loss function and the regularization term of equipment fault diagnosis, deep learning is playing an traditional sparse PCA, the JSPCA based fault detection increasingly important role in the process of fault method achieves sparse feature selection and robust diagnosis, during which the timeliness requirement is fault detection simultaneously without high computation high and the fault diagnosis results need to be obtained cost. The effectiveness of the proposed method is accurately and timely. However, with the increase of evaluated on the Tennessee Eastman process. network layers, the training time of deep learning model becomes longer. Learning rate in the deep learning 15:50-17:50 SunC07-6 model plays an important role in the process of model An Improved Granger Causal Analysis Framework training, and a well-designed learning rate adjustment Based on Redundancy Index strategy can effectively reduce the training time and Fei Wang Shanghai Univ. satisfy the requirements of fault diagnosis. At present, Jian-Guo Wang Shanghai Univ. some deep learning models usually adopt a globally Xiang-Yun Ye Shanghai Univ. uniform learning rate strategy, which is unreasonable for Yuan Yao National Tsing-Hua Univ. different parameters. This paper has designed an Jun-Jiang Liu Baoshan Iron & Steel Co., Ltd. adaptive learning rate strategy for the parameters of weight and bias respectively in deep learning model. The Granger causality is a very effective method for the Specifically, the strategy contains a learning rate root cause diagnosis. However, the synergy and strategy based on stochastic gradient descent method redundancy between variables will affect the accuracy of for weight, and a power exponential learning rate the test result. This article introduces an unnormalized strategy for bias. Experiments are carried out to validate Granger causality method to detect the synergy and the effectiveness of proposed learning rate strategy.

77 redundancy between variables and defined a This paper focuses on the identification problem for redundancy index. Based on the index, a variable finite impulse response systems through using the stratification method is proposed. The layered result hierarchical identification principle. Based on the obtained by this method can directly reflect the order in hierarchical identification principle, the hierarchical which the variables are affected by the fault. In addition, based least squares iterative algorithm is proposed to further Granger causality tests based on the layered estimate the parameters of the two-input single-output results can make fault propagation paths more accurate. Hammerstein finite impulse response systems. Finally, a Two typical faults of the TE process were used to verify simulation example is given to test the effectiveness of the effectiveness of the method. the proposed algorithm.

15:50-17:50 SunC07-7 15:50-17:50 SunC07-9 Early Fault Feature Extraction of Nuclear Main Pump Discrete-time Adaptive ILC for Uncertain Systems with Based on MEMD-1.5 dimensional Teager Energy Unknown Nonlinear Dead Zone Inputs and Control Spectrum Directions Shule Li Beijing Information Sci. & Tech. Univ. Qing-yuan Xu Guangdong Polytechnic Normal Univ. Jie Ma Beijing Information Sci. & Tech. Univ. Ya Li Guangdong Polytechnic Normal Univ. Jing Cheng Guangdong Polytechnic Normal Univ. For the weak component in the early failure of the Tengfei Xiao Sun Yat-sen Univ. nuclear main pump, it is easy to be masked by strong faults or overwhelmed by strong noise to cause leakage A discrete-time adaptive iterative learning control (ILC) diagnosis, and in actual working condition with Nussbaum function for systems with uncertain measurement, multiple sensors are usually used to nonlinear dead zone inputs and control directions is synchronize the signals. The existing traditional feature considered in this work. The adaptive ILC is based on a analysis methods are only the single-channel vibration fuzzy rules emulated network and an adaptive item, signal measured by multi-sensors is processed, and the where the unknown nonlinear dead zone input as well as multi-channel data fusion is not performed at the later iteration-varying uncertainties can be well compensated. stage to achieve the multi-channel synchronization Through rigorous analysis, the convergence of the correlation analysis. A multi-dimensional empirical proposed discrete-time adaptive ILC is achieved and the mode decomposition (Multivariate Empirical Mode boundedness of system signals is guaranteed. In the Decomposition, MEMD)-1.5-dimensional Teager energy end, the behaviors of the proposed ILC method are spectrum is proposed for the extraction of micro-fault presented by a simulation example. features. Firstly, use the MEMD to adaptively decompose the multi-channel vibration signals on the collected 15:50-17:50 SunC07-10 multi-channel fault characteristic signals under the same Intelligent Measurement Modeling Using a Novel state to obtain the Intrinsic Mode Functions (IMF) Multi-nonlinear Mapping Based Extreme Learning components of each channel, and then calculate the Machine Integrated with Partial Least Square Regression kurtosis value and correlation coefficient of each Qunxiong Zhu Beijing Univ. of Chemical Tech. channel IMF component to select the best IMF Xiaohan Zhang Beijing Univ. of Chemical Tech. component containing the main information of the fault. Yuan Xu Beijing Univ. of Chemical Tech. Finally, the 1.5-dimensional Teager energy spectrum is Yanlin He Beijing Univ. of Chemical Tech. used to obtain the fault characteristic information of the signal to achieve the extraction of minor fault features. Accurate intelligent measurement modeling plays a key In order to verify the feasibility of the theory, simulation role in complex process industries. However, tests are carried out and the method is applied to the establishing an accurate and robust measurement early failure of the outer ring of the bearing, and model tends to be more and more difficult because of compared with EMD and envelope demodulation, it is the increasing complexity in terms of nonlinearity and verified that this method can effectively deal with early collinearity of data. To solve this problem, a novel multi-channel failure information of rotating machinery. multi-nonlinear mapping based extreme learning It has theoretical guidance significance for early machine integrated with partial least square regression diagnosis of small faults of nuclear main pump. is proposed in this paper. In the proposed model, two problems of nonlinearity and collinearity are effectively 15:50-17:50 SunC07-8 dealt with by using multi-nonlinear mapping and partial Hierarchical least squares based iterative algorithms for least square regression, respectively. For evaluating two-input single-output Hammerstein ﬁnite impulse performance, empirical studies on a commonly used response systems bench mark problem and a real-world application Liangbin Sha Qingdao Univ. of Sci. & Tech. confirm that the presented method can obtain high Yan Ji Qingdao Univ. of Sci. & Tech. accuracy and high stability performance for intelligent measurement.

DDCLS’20

15:50-17:50 SunC07-11 Haiguo Tang State Grid Corp. of Hunan Electric Power Co. The Application of Adaptive A-star Algorithm in Layout Research Institute of Spatial Pipeline Min Fan Chongqing Univ. Xinyu Zhao Southwest Jiaotong Univ. Yaling Liu Chongqing Univ. Na Qin Southwest Jiaotong Univ. Qing Yang Chongqing Univ. Jie Huang Nuclear Power Institute of China Yongchen Miao Harbin Shipping School Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and In order to settle the complex problem of layout of operation of DAS, studying on the optimization of DAS spatial pipeline, the diameter of pipeline is taken as the has received much attention from both academia and distance between adjacent scattered points, and the industry. This study proposed cluster-based relevance spatial layout including obstacles is approximated as a analysis between construction mode and benefit-cost three-dimensional diagram of scattered points. A-star ratio (BCR) for optimizing the DAS. This method clusters algorithm is used to search an optimal pipelines path the DASs of different cities, and analyzes the clustering from the three-dimensional diagram of scattered points. results. Then, the method analyzes BCR of DAS in However, when traditional A-star algorithm addresses different categories and uses comparative analysis, grey pre-selected points that are the same cost, it selects relational analysis (GRA) and sensitivity analysis to pre-selected points randomly. In view of the analyze the relevance between the construction mode disadvantages, an adaptive A-star algorithm is proposed and BCR of typical city’s DAS and determine the major based on the traditional A-star algorithm, in this paper. factors affecting the BCR comprehensively. Finally, it The adaptive A-star algorithm selects more reasonable integrates all analysis results to make optimization pre-selected point according to the surrounding suggestions for DAS. The case study shows that the environment and situation of pre-selected point, so as to method is effective and feasible, which can provide achieve the requirements of the shortest pipeline and direction and reference basis for the optimization of DAS the fewest inflection point. Finally, the experimental in cities of different scale. simulation shows that the adaptive A-star algorithm has better performance than the traditional A-star algorithm. 15:50-17:50 SunC07-14 Application of Gradient Tree in Credit Evaluation of 15:50-17:50 SunC07-12 Chinese Peasant Households An Evaluation Method of Autonomy for Marine Wen Xiao Anhui Univ. of Finance & Economics Unmanned Vehicles Zhihui Yang Anhui Univ. of Finance & Economics Lei Shi China Institute of Marine Tech. & Economy Jiabin Chen China Institute of Marine Tech. & Economy The traditional peasant household credit evaluation is Jiexin Hu China Institute of Marine Tech. & Economy not only subjective, but also complex, time-consuming Huiling Chen China Institute of Marine Tech. & Economy and inefficient with the help of experts' experience. In Qiang Ma China Institute of Marine Tech. & Economy this paper, the research data of China family survey and Ya Guo China Institute of Marine Tech. & Economy research center are used to establish an evaluation model of China's peasant household credit with the help With the maturity of unmanned technology, unmanned of the gradient tree model in the machine learning systems are widely used in various fields, and the algorithm, and the AUC and F1 score are used to verify autonomy level has become the focus of designers and the evaluation results of the model. The empirical results users. How to evaluate the autonomy level of unmanned show that the gradient tree model has a good prediction system accurately and reasonably has become the focus ability in peasant household credit evaluation, and it can of research. In this paper, the concept of autonomy is be used as a new evaluation method in peasant studied. Then, the evaluation methods of autonomy for household credit evaluation in China. unmanned system are reviewed in the order of the level evaluation method, the axis method, the fuzzy evaluation 15:50-17:50 SunC07-15 method. According to the study results, an evaluation Location and Detection Method of Ring-shaped Culture method of autonomy for marine unmanned vehicle is Carrier for Nucleic Acid Detection proposed finally. Jiarui Cui Univ. of Sci. & Tech. Beijing Jiawei Wang Univ. of Sci. & Tech. Beijing 15:50-17:50 SunC07-13 Qing Li Univ. of Sci. & Tech. Beijing Cluster-based Relevance Analysis between Peng Lv Univ. of Sci. & Tech. Beijing Construction Mode and BCR for Optimizing the DAS Xiangquan Li Univ. of Sci. & Tech. Beijing Jiran Zhu Tongji Univ. Lixin Zhang Univ. of Sci. & Tech. Beijing Hua Leng Hunan Univ. This paper proposes a detection and location method for a special culture carrier for Nucleic Acid Detection in 79

COVID-19. In order to reduce the pollution caused by to simplify the model, making the whole process clear, manual operation and promote the automation of easy to understand. The practices described herein Nucleic Acid Detection, we use the image processing provide one set of practical strategies and tools for and machine vision techniques to detect and locate the potential use. target culture carrier. By using the OpenCV library functions, we can complete the detection process of 15:50-17:50 SunC07-18 image processing algorithm. Based on the recognition An Abrasion Detection Method for Elevator Traction Wire and size measurement of the existing training carrier, the Rope based on Template Matching appropriate threshold is obtained through many Zehua Li Zhejiang Univ. experiments to complete the detection and location of Zheng Chai Zhejiang Univ. the target culture carrier. Finally, we gather 10 pictures of Chunhui Zhao Zhejiang Univ. images for detection and evaluation, the feasibility and efficiency of this method is demonstrated through the actual detection operation. Elevators are gradually becoming an indispensable part of people's daily life. At the same time, various safety 15:50-17:50 SunC07-16 problems caused by elevators are also seriously threatening people's life and property security. Elevator Visual Navigation Algorithm of Small Body Lander traction wire rope damage is a common type of potential Based on UFIR Filter safety hazard. At present, the defect detection method Wei Shao Qingdao Univ. of Sci. & Tech. for elevator traction wire rope damage generally has Hanxue Zhao Qingdao Univ. of Sci. & Tech. high complexity and labor cost. This paper presents a Lingfei Dou Qingdao Univ. of Sci. & Tech. method to detect the abrasion of elevator traction wire Boning Wang Qingdao Univ. of Sci. & Tech. rope by taking full use of the texture feature of the Guangze Wang Qingdao Univ. of Sci. & Tech. traction wire rope images. First, the interference factors Wenlong Yao Qingdao Univ. of Sci. & Tech. in the process of image acquisition are removed by the pretreatment methods of graying and denoising. Then, The gravity of small body is small and its distribution is the abrasion area of the traction wire rope is determined not uniform. Therefore, for small body lander, the according to the similarity measurement criteria by process noise in traditional Kalman filters is difficult to combining edge detection and template matching. count. In addition, due to the complex environment of Finally, the image is finely segmented and the abrasion deep space, there is no guarantee that the visual rates of different areas are calculated. This method is navigation measurement noise must be Gaussian white convenient and intuitive to detect the changes of noise, and it is difficult to calculate statistical knowledge elevator wire rope abrasion. On one hand, the such as its covariance. These problems will affect the complexity of the detection and the loss of the human estimation accuracy of the Kalman filter. In this regard, labor can be reduced; on the other hand, valuable this paper combines a crater-based navigation reference can be provided for elevator maintenance. algorithm, uses the UFIR filter that does not require Experiment on actual wire rope images validate the process and measurement noise statistics. Colored effectiveness of the proposed method. noise is added to process equation and measurement equation for simulation, and compare the estimation 15:50-17:50 SunC07-19 errors of Kalman filter and UFIR filter. The results show that when the process noise and measurement noise are The use of Convolutional Neural Network for Malware non-Gaussian white noise or the prior statistical Classification knowledge of noise statistics is inaccurate. The UFIR Shahrukh Sajjad Bohai Univ. filter has better estimation effect and is more robust. Bi Jiana Bohai Univ. Shah Zaib sajjad NFC Institute of Engineering & Tech. 15:50-17:50 SunC07-17 Data Analysis and Practical Strategies on Opioid Crisis Digital security is confronting an immense risk from Donghao Yang Huaibei normal Univ. malware or malicious software. In recent years, there Zhihui Yang Anhui Univ. of Finance & Economics has been an increase in the volume of malware, reaching above 980 million in 2019*. To identify and classify this In view of the time-dependent spread and characteristics pernicious software, complex details and patterns of synthetic opioids and heroin reported between five among them are to be gathered, segregated, and states and their counties in the US, aiming at the analyzed. In this regard, Convolutional Neural Networks correlation analysis between opioid-like use and the (CNN) – an architecture of Deep Neural Networks (DDN) social and economic data of the US census, the optimal can offer a more efficient and accurate solution than C5.0 algorithm and data flow network model are used to Conventional Neural Network (NN) systems. In this model the decision tree, and the quantitative results of paper, we have looked into the consequences of using the related factors of opioid-like use are obtained. We try conventional NN systems and the benefits of using CNN on a sample of malware provided by Microsoft. In 2015, 80

DDCLS’20

Microsoft announced a malware classification challenge The ARIMA model is reliable for the quality fault and released more than 21,000 malware samples. Many diagnosis. Numerical simulations show that the model interesting solutions were put forward by scientists and can accurately describe the change of quality overtime students around the world. Inspired by their efforts we when a fault occurred, and can make judgment and early also have put forward a method. We have converted the warning of the fault in a short period of time. malware binary files into images and then trained a CNN model for identification and categorization of this 15:50-17:50 SunC07-22 malware to their respective families. From this method, A Lateral Active Collision Avoidance System Based on we achieved a high percentage accuracy of 98.88%. Fuzzy-PID and Sliding Mode Control for Electric Vehicles Yu Tian Changchun Univ. of Tech. 15:50-17:50 SunC07-20 Yufeng Lian Changchun Univ. of Tech. Granger Causality Detection Based on Neural Network Taotao Zhang Changchun Univ. of Tech. Jing-Ru Su Shanghai Univ. Chonghe Tang Siemens Factory Automation Jian-Guo Wang Shanghai Univ. Engineering Ltd. Long-Fei Deng Shanghai Univ. Shi Qi Faw tooling die manufacturing Co., Ltd. Yuan Yao National Tsing-Hua Univ. Jian-Long Liu Shanghai Minghua Electric Power Sci. & A lateral active collision avoidance system based on Tech. Co., Ltd. fuzzy and slide mode control is presented for electric vehicles in this paper. It can be consisted of the safety Plant-wide oscillations are very common in industrial distance model, the upper level controller, and the lower processes. When a control unit oscillates during the level controller. The safety distance model can judge and process, the oscillations will propagate through the give current vehicle’s steering collision avoidance safety connectivity between the units, which will cause poor logic. When an emergency danger occurs, the steering product quality and higher energy consumption. It is collision avoidance can be considered first, rather than important to diagnose the root cause of plant-wide braking collision avoidance. The upper level controller oscillations. Generally, methods for estimating Granger can be designed based on fuzzy control. It can calculate causality use linear models such as autoregressive the desired trajectory. The lower level controller can be models. This paper proposes using Granger causality signed based on slide mode control to realize the direct analysis based on the neural network for root cause yaw moment control. Simulation experiments can be diagnosis, which effectively solves the problem that conducted with different preview times and different Granger causality analysis based on linear models control methods. Simulation results demonstrate that cannot handle non-linear data. The Granger causality the lateral active collision avoidance system based on detection model based on neural network is fuzzy and slide control is in good agreement with the successfully applied to the plant-wide oscillation root actual driving process for electric vehicles. location of industrial process, and the correct root cause is detected, which proves the feasibility and 15:50-17:50 SunC07-23 effectiveness of the method. On Driver's Workload in Dangerous Scenes Based on EEG Data 15:50-17:50 SunC07-21 Jiyuan Tan North China Univ. of Tech. Application of ARIMA Model in Fault Diagnosis of TEP Rui Bi North China Univ. of Tech. Wei Mu Bohai Univ. Weiwei Guo North China Univ. of Tech. Aihua Zhang Bohai Univ. Li Li Tsinghua Univ. Wenxiao Gao Bohai Univ. Yueqin Wang North China Univ. of Tech. Xing Huo Bohai Univ. Scientific measurement of the risk degree of traffic In fault prediction, ARMA is a commonly used and scenes and accurate assessment of driver's workload important method to study time series, but it also has are conducive to reducing driving risk and road traffic some problems. When time series is non-stationary, accidents. In this paper, EEG signal evaluation method ARMA prediction effect is not accurate. Therefore, on based on “driver's” perspective is used to describe the this basis, the ARIMA model is used to transform the risk of traffic scene objectively and quantitatively. The non-stationary time series into stationary time series by traffic scenes with dynamic traffic environment factors the difference method. In view of the three fault states in are taken as the research objects, including the TEP quality, the ARIMA model is established by using pedestrian scene and the variable-speed vehicle scene. Python, and a TEP quality fault diagnosis system is The drivers’ EEG signals are used as the indicators to further established, and the quality variables are evaluate the risk degree of the traffic scene. Based on predicted. The results show that ARIMA model can research objects and indicators, the internal relationship predict the quality of TEP in a short time, which has the between drivers' EEG signals and traffic dangerous advantages of simple modeling and accurate prediction. environment factors are explored, and the evaluation

81 models of traffic scene risk degree based on drivers' Zhiying Sun Beijing Univ. of Chemical Tech. EEG signals are established. Jinglin Zhou Beijing Univ. of Chemical Tech.

15:50-17:50 SunC07-24 In order to improve the accuracy of the regression Event-triggered based consensus control for a type of model, an L1 norm partial least square method multi-agent systems (IELM-L1-PLS) based on an incremental limit learning Jiantao Shi Nanjing Research Institute of Electronic Tech. machine is proposed. The data processing process of the incremental limit learning machine is nested into the In this note, the consensus control problem has been framework based on the L1 norm partial least square researched for a type of leader-follower multi-agent method, and the original data is upgraded by extracting systems by using the event-triggered strategy. In order the hidden node output matrix in the incremental limit to eliminate continuous information transmission learning machine, and then Regression analysis was between the neighboring agents or nodes, the performed on the upgraded data using L1-PLS. This consensus controller is constructed by using the method is used for experimental verification of actual estimated state information of neighboring agents data. The results show that the L1-PLS method based on instead of their real states. The communication instants the incremental limit learning machine can perform are determined by the developed event-triggered better regression analysis on the data. strategy to minimize the amount of communication between neighboring agents. A type of error 15:50-17:50 SunC07-27 convergence analysis based on Lyapunov function has Fault-Tolerant Control for Flexible Air-Breathing been provided to prove the bounded convergence of the Hypersonic Vehicle Based on Tube Robust Model proposed consensus scheme. Finally, a simulation case Predictive Control is given to verify the effectiveness of the given Xiaohe Yang Tianjin Univ. event-based consensus control strategy. Weijie Lv Tianjin Univ. Chaofang Hu Tianjin Univ. 15:50-17:50 SunC07-25 Yongtai Hu Aviation Key Laboratory of Sci. & Tech. A Quality-related Fault Detection Method Based on on Aircraft Control Weighted Mutual Information Songjun Zhong Beijing Univ. of Chemical Tech. This paper adopts Tube robust model predictive control Jinglin Zhou Beijing Univ. of Chemical Tech. (RMPC) approach to achieve the trajectory tracking control for flexible air-breathing hypersonic vehicle Quality-related fault detection has become a research (FAHV) with lose efficiency fault of actuator. First of all, hotspot in recent years, and its goal is to maximize the Jacobian linearization and tensor-product alarm rate for quality-related faults in process transformation are used to build the polytopic linear monitoring and minimize the alarm rate for faults that are parameter varying (LPV) model for FAHV. Then, the irrelevant or self-adjustable. The traditional principal FAHV faulty model is obtained by transforming lose component analysis (PCA) and partial least squares efficiency fault of actuator into the additional (PLS) method use covariance to extract the principal disturbance term. Thirdly, fault-tolerant control is component. As a second-order statistic, covariance can realized by composite control law. The error system is only extract Gaussian information without considering obtained by the difference between the actual and the that the data may contain higher-order non-Gaussian nominal system. By designing the minimum robust information, so its statistical alarm rate is not the most positive invariant set (mRPI), the error system state is accurate. To solve above problems, this paper proposes limited to the mRPI set. The additional disturbance term a quality-related fault detection method based on in mRPI set is suppressed by the auxiliary robust weighted mutual information. The first step is to extract feedback control law. The nominal control law provides a the set of process variables that contain the most reasonable trajectory for the actual system. And it is information about the quality variables through calculated by the convex optimization problem, which is Bayesian fusion mutual information, and then use the constrained by linear matrix inequality (LMI). The fusion mutual information for the extracted process fault-tolerant controller guarantees that the actual variables to eliminate some process variables. For the trajectory is located in the mRPI set all the time and remaining process variables, use PLS algorithm based centered on the nominal trajectory. Finally, the designed on maximum mutual information to extract principal Tube RMPC controller is validated by simulation. components for statistical modeling. Finally, it is applied to Tennessee Eastman process (TEP) to simulate the 15:50-17:50 SunC07-28 feasibility and effectiveness of the proposed method. WCE Polyp Detection Based On Locality-Constrained Linear Coding With A Shared Codebook 15:50-17:50 SunC07-26 Tingwei Zhu Zhejiang Univ. of Tech. L1-PLS Based on Incremental Extreme Learning Machine Sheng Li Zhejiang Univ. of Tech.

DDCLS’20

Xiongxiong He Zhejiang Univ. of Tech. Jian-Guo Wang Shanghai Univ. Min Yu Zhejiang Univ. of Tech. Guo-Qiang Zhao Shanghai Univ. Jianjun Yang Zhejiang Univ. of Tech. Yuan Yao National Tsing-Hua Univ. Chao Xu Baoshan Iron & Steel Co., Ltd. Locality-constrained Linear Coding (LLC) has been proven to have good results in image classification. The endpoint temperature of the molten steel in the VD However, the codebook used is simply obtained by (Vacuum Degassing) furnace is an important parameter K-means without any other distinctive properties which determining the quality of the finished steel products. limits classification performance. Our paper targets Based on the in-depth analysis of the vacuum refining Wireless Capsule Endoscopy (WCE) images process of the VD furnace, combined with the field data, classification by learning a shared codebook in the LLC. the effective preprocessing of the data was completed. The main idea of this work is try to use the part of Then the NNG (Non-Negative Garrote) variable selection codebook that represent private features as much as algorithm is used to determine the input variables. An possible. The proposed method rearrange the columns integrated ELM (Extreme Learning Machine) molten steel of the codebook that arrange these rarely used atoms in endpoint temperature modeling method based on the bottom of the updated codebook which called shared AdaBoost.RT is proposed. Experimental simulation codebook. Then we encode the local features through results show that the AdaBoost.RT-ELM prediction the shared codebook. Finally, these codes could be model has significantly improved prediction accuracy trained and classified by the Support Vector Machine than a single ELM. (SVM). The experiment results indicate a better performance of proposed method compared with some 15:50-17:50 SunC07-31 existing approaches. Design of Spatial Repetitive Controller with Fractional 15:50-17:50 SunC07-29 FIR Filter Low-rank Shared Dictionary Learning with Incoherence Bo Wang Harbin Institute of Tech. Constraint for Endoscopic Gastrointestinal Image Xin Huo Harbin Institute of Tech. Classification Lu Xu Harbin Institute of Tech. Yue Ma Zhejiang Univ. of Tech. Zimo Xu Shanghai Aerospace Control Tech. Zixin Shen Zhejiang Univ. of Tech. Institute Sheng Li Zhejiang Univ. of Tech. Liping Chang Zhejiang Univ. of Tech. Rate turnable is a high precision position servo system. Jinhui Zhu The Second Affiliated Hospital of Due to the existence of spatially periodic disturbances, Zhejiang Univ. School of Medicine the control accuracy and riding quality of rate turnable Xiongxiong He Zhejiang Univ. of Tech. are affected. In this paper, the spatial repetitive controller is introduced to reject spatially periodic disturbances. Endoscope has been widely used in clinical examination Spatially periodic disturbances of rate turnable system of gastrointestinal diseases. Many automatic are analyzed. Traditional repetitive controller is talked endoscopic image classification algorithms based on about and the design method of fractional FIR filter dictionary learning are proposed to assist doctors in based on window function is given. Based on these, diagnosing diseases, where the learning method of repetitive controller design method of fractional FIR filter shared dictionary and class-specific dictionaries based on window function is proposed. However, the enables training dictionary to be more discriminative. speed changes in systems, the period of disturbances Nevertheless, in the process of dictionary learning, the also changes. For this problem, the spatial repetitive appearance of common features in class-specific controller with fractional FIR filter is presented. The dictionaries may cause low classification accuracy. To simulation results are made to illustrate the remedy this deficiency, herein we introduce a coherence effectiveness of the spatial repetitive controller. constraint between low-rank shared dictionary and class-specific dictionaries. The proposed dictionary learning method is applied to the classification system of endoscopic gastrointestinal images, including normal, polyp and ulcer images, whose experimental results prove that it has promising classification performance.

15:50-17:50 SunC07-30 Endpoint Temperature Prediction of Molten Steel in VD Furnace Based on AdaBoost.RT-ELM Zeng Chen Shanghai Univ.

Saturday, November 21, 2020, Liuzhou Liudong Ramada Plaza Hotel, Liuzhou (柳州柳东华美达广场酒店) 8:00-8:30 Opening ceremony, Venue: Dongcheng Hall, Chair: Prof. Jing Wang 8:30-9:30 Keynote Address 1: Reinforcement Learning for Optimal Control, Prof. Derong Liu, Venue: Dongcheng Hall, Chair: Prof. Chenghong Wang 9:30-10:30 Keynote Address 2: Fully Autonomous UAS and Its Applications , Prof. Ben M. Chen, Venue: Dongcheng Hall, Chair: Prof. Huaguang Zhang 10:30-11:00 Tea Break and Photo 11:00-12:00 Keynote Address 3：Data and/or Control – Is Control Theory Becoming Obsolete? Prof. Frank Allgöwer, Venue: Dongcheng Hall, Chair: Prof. Mingxuan Sun 12:00-13:30 Lunch Time/Room VIP 23 Hall Room 1 Room 2 Room 3 Room 4 Room 5 Room 6 13:30-15:30 Distinguished Lecture SatA01 SatA02 SatA03 SatA04 SatA05 SatA06 Data-Driven Wide-Range Nonstationary Process Monitoring, 13:30-14:00 Prof. Chunhui Zhao, Chair: Prof. Ronghu Chi Distributed Gradient Tracking for Optimization and Learning Data-driven fault IS：Data-driven IS：Reinforcement 14:00-14:30 IS：RNN for over Networks, Prof. Keyou You, Chair: Prof. Jing Na Model-free adaptive diagnosis and adaptive control for learning and Data driven control computing and its Prediction and Scheduling for Industrial Energy System, control health maintenance uncertain nonlinear intelligent 14:30-15:00 robotic applications Prof. Jun Zhao, Chair: Prof. Shan Liu (I) systems decision-making Data Driven Brain Neurofiber Tract Identification, Prof. 15:00-15:30 Yuanjing Feng, Chair: Prof. Dong Shen 15:30-15:50 Tea Break Time/Room VIP 23 Hall Room 1 Room 2 Room 3 Room 4 Room 5 Room 6 SatB07 SatB01 SatB02 SatB03 SatB04 SatB05 SatB06 Data-driven fault IS：Data-driven IS：Advanced Neural networks, 15:50-17:50 ADRC technology and Iterative learning diagnosis and smart intelligent control fuzzy systems Best Paper Award Finalist applications control (I) health maintenance transportation and method and its control in data (II) its application application driven manner 18:00-20:00 Dinner Sunday, November 22, 2020, Liuzhou Liudong Ramada Plaza Hotel, Liuzhou (柳州柳东华美达广场酒店) 8:30-9:30 Keynote Address 4：Data-Driven Model Reduction, Prof. Alessandro Astolfi, Venue: VIP 23 Hall, Chair: Prof. Zengqiang Chen 9:30-9:50 Tea Break Time/Room VIP 23 Hall Room 1 Room 2 Room 3 Room 4 Room 5 Room 6 9:50-11:50 Distinguished Lecture SunA01 SunA02 SunA03 SunA04 SunA05 SunA06 Non-Intrusive Modeling for We-Energy based on 9:50-10:20 Mechanism-Data Hybrid Drive, Prof. Qiuye Sun, Chair: Prof. Darong Huang IS：Data-driven fault Dynamic Reference Programming-Based Model Predictive Data-driven IS: Higher order Statistical learning Pattern Control by Dynamic Controlled PCA, Prof. Xiaoli IS: Data-driven IS：Iterative learning diagnosis, 10:20-10:50 modeling, differential and machine Luan, Chair: Prof. Xuhui Bu technologies and its control and it's intelligent control optimization and feedback control learning in Recent Advances in Remaining Useful Life Prediction and applications scheduling and ADRC automation field (I) applications and public security 10:50-11:20 Health Management Technology, Prof. Xiaosheng Si, Chair: of network traffic Prof. Zhiqiang Ge Iterative Learning Control with Incomplete Information, Prof. 11:20-11:50 Dong Shen, Chair: Prof. Deqing Huang Lunch Time/Room VIP 23 Hall Room 1 Room 2 Room 3 Room 4 Room 5 Room 6 SunB07 SunB01 SunB02 SunB03 SunB04 SunB05 SunB06 Applications of Data-driven fault IS：Data-driven IS：Distributed IS：Identification and Iterative learning 13:30-15:30 data-driven methods diagnosis and Interactive session (I) control (II) identification, learning and control adaptive control for to industrial health maintenance optimization and of networked systems nonlinear mechanical processes (III) control (I) systems 15:30-15:50 Tea Break Time/Room VIP 23 Hall Room 1 Room 2 Room 3 Room 4 Room 5 Room 6 SunC07 SunC01 SunC02 SunC03 SunC04 SunC05 SunC06 Statistical learning IS：Distributed IS：Dynamic Data-driven fault IS：New trends in 15:50-17:50 IS：ADP and RL for and machine learning and control Interactive session (II) linearization based diagnosis and health data driven and optimal control learning in of networked systems data-driven control maintenance (IV) Industrial AI control automation field (II) (II) 18:00-20:00 Closing Ceremony and Banquet, Chair: Prof. Dong Shen