DOCSLIB.ORG

Application of Reinforcement Learning Techniques to the Development of Flight Control Systems for Miniature Uav Rotorcraft

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Application of Reinforcement Learning Techniques to the Development of Flight Control Systems for Miniature Uav Rotorcraft MACHINE LEARNING FOR INTELLIGENT CONTROL: APPLICATION OF REINFORCEMENT LEARNING TECHNIQUES TO THE DEVELOPMENT OF FLIGHT CONTROL SYSTEMS FOR MINIATURE UAV ROTORCRAFT A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Engineering in Mechanical Engineering in the University of Canterbury by Edwin Hayes University of Canterbury 2013 Abstract This thesis investigates the possibility of using reinforcement learning (RL) techniques to create a flight controller for a quadrotor Micro Aerial Vehicle (MAV). A capable flight control system is a core requirement of any unmanned aerial vehicle. The challenging and diverse applications in which MAVs are destined to be used, mean that consider- able time and effort need to be put into designing and commissioning suitable flight controllers. It is proposed that reinforcement learning, a subset of machine learning, could be used to address some of the practical difficulties. While much research has delved into RL in unmanned aerial vehicle applications, this work has tended to ignore low level motion control, or been concerned only in off-line learning regimes. This thesis addresses an area in which accessible information is scarce: the performance of RL when used for on-policy motion control. Trying out a candidate algorithm on a real MAV is a simple but expensive proposition. In place of such an approach, this research details the development of a suitable simulator environment, in which a prototype controller might be evaluated. Then inquiry then proposes a possible RL-based control system, utilising the Q-learning algorithm, with an adaptive RBF-network providing function approximation. The operation of this prototypical control system is then tested in detail, to determine both the absolute level of performance which can be expected, and the effect which tuning critical parameters of the algorithm has on the functioning of the controller. Performance is compared against a conventional PID controller to maximise the usability of the results by a wide audience. Testing considers behaviour in the presence of disturbances, and run-time changes in plant dynamics. Results show that given sufficient learning opportunity, a RL-based control system performs as well as a simple PID controller. However, unstable behaviour during learning is an issue for future analysis. Additionally, preliminary testing is performed to evaluate the feasibility of implementing RL algorithms in an embedded computing environment, as a general requirement for a MAV flight controller. Whilst the algorithm runs successfully in an embedded context, observation reveals further development would be necessary to reduce computation time to a level where a controller was able to update sufficiently quickly for a real-time motion control application. In summary, the study provides a critical assessment of the feasibility of using RL algorithms for motion control tasks, such as MAV flight control. Advantages which merit interest are exposed, though practical considerations suggest at this stage, that such a control system is not a realistic proposition. There is a discussion of avenues which may uncover possibilities to surmount these challenges. This investigation will prove useful for engineers interested in the opportunities which reinforcement learning techniques represent. i ii Contents 1 Introduction 1 1.1 Motivation . 1 1.1.1 UASs, MAVs & Autopilots . 1 1.1.2 Issues with Traditional Control Systems . 1 1.1.3 Possible Application of Machine Learning . 2 1.1.4 Similar Research & Objectives . 3 1.2 Outline . 3 1.2.1 Problem Domain . 3 1.2.2 Thesis Summary . 4 1.2.3 Limitations . 4 2 Background & Literature 5 2.1 Background of Machine Learning in General . 5 2.2 Background of Reinforcement Learning . 6 2.2.1 About Reinforcement Learning . 6 2.2.2 About Q-Learning . 7 2.2.3 About Eligibility Traces . 8 2.2.4 Q-Learning Function Estimation via ARBFN . 8 2.3 Reinforcement Learning for UAV Platforms . 9 2.3.1 Remaining Questions . 11 2.4 A Note on Terminology . 11 3 Design of Micro Aerial Vehicles 13 3.1 About Quadrotor MAVs . 13 3.1.1 General Scheme . 13 3.1.2 Basic Dynamics . 14 3.1.3 A Typical Quadrotor UAV . 15 3.1.4 Test Stand . 15 4 Software Design 19 4.1 Computing Software Technologies . 19 4.1.1 Java . 19 4.1.2 Reinforcement Learning Library (Teachingbox) . 20 4.1.3 J3D Robot Simulator (Simbad) . 21 4.2 Quadrotor UAV Simulator . 22 4.2.1 Encoding of Simulator State . 22 4.2.2 Freedom During Testing . 23 4.2.3 Encoding of Controller State . 24 4.2.4 Throttle Signal Mixing . 25 4.2.5 Simulator Implementation . 26 4.3 Controllers . 29 iii 4.3.1 Traditional PID Controller . 30 4.3.2 Q-Learning Controller . 31 4.4 Integration of Components . 35 4.4.1 Modifications Required . 35 4.4.2 Typical Call Chain . 37 5 Performance of PID Controller 39 5.1 Experimental Details . 39 5.1.1 Test Procedure . 39 5.1.2 Performance Metrics . 40 5.2 Results . 40 5.2.1 A First Attempt . 40 5.2.2 Other Controller Gains . 42 5.2.3 Effects of Disturbance . 45 5.2.4 Handling Changes in Dynamics . 46 6 Performance of Q-Learning Based Controller 53 6.1 Experimental Details . 53 6.1.1 Test Procedure . 53 6.1.2 Performance Metrics . 54 6.1.3 The Term `Stability' . 54 6.2 Tuning Parameters . 55 6.2.1 Initial Baseline . 55 6.2.2 Varying Learning Rate α ........................... 60 6.2.3 Varying Discount Rate γ ........................... 66 6.2.4 Varying Eligibility Trace Decay Rate λ .................... 73 6.2.5 Varying Velocity Reward Gain Kθ_ ...................... 81 6.2.6 Varying Displacement ARBFN Resolution σθ . 92 6.2.7 Varying Velocity ARBFN Resolution σθ_ . 100 6.2.8 Varying Greediness ..............................107 6.2.9 Final Baseline . 112 6.3 Effects of Disturbance . 118 6.4 Handling Changes in Dynamics . 121 6.4.1 Without Disturbances . 121 6.4.2 With Disturbances . 127 6.5 Inspection of Q-Functions and Policies . 133 6.6 Assessment of Results . 137 7 Computational Considerations 139 7.1 Introduction . 139 7.1.1 Importance of Computational Performance . 139 7.1.2 Practical Considerations . 140 7.2 Computing Hardware Technologies . 140 7.2.1 Desktop Computer . 140 7.2.2 Embedded ARM Computer . 141 7.3 Performance Comparison . 142 7.3.1 Performance Metrics . 142 7.3.2 Experimental Procedure . 143 7.3.3 Expectations . 143 7.3.4 Results . 144 8 Conclusions 147 iv 8.1 Considerations for Parameter Selection . 147 8.2 Viability for Practical Use . 148 8.3 Further Expansion & Future Work . 149 8.3.1 Longer Simulations . 150 8.3.2 More Comprehensive Test Regime . 150 8.3.3 Testing on Test Platform . 150 8.3.4 Free Rotation . 151 8.3.5 Incorporating Translational Control . 151 8.3.6 Adding Multiple Actions & Continuous Actions . 151 8.3.7 Hybrid Approaches . 152 8.4 Closing Remarks . 153 A Additional Q-Function and Policy Plots 155 Bibliography 165 v vi List of Figures 3.1 Illustrative diagram of simplified model of forces on quadrotor MAV. 14 3.2 Illustrative diagram of principles for movement of a quadrotor MAV. 16 3.3 The Droidworx CX-4, a fairly typical quadrotor MAV intended for industrial applications. 16 3.4 The UAV test stand. The X/Y translational arm is denoted Details A and B. The translational Z slide is denoted Detail C, and the rotational gimbal frame is denoted Detail D. 18 4.1 Diagram showing the global/earth reference frame (denoted E) and the base/vehicle reference.
Load more

Recommended publications
  • Handbook of Intelligent Control
    HANDBOOK OF INTELLIGENT CONTROL NEURAL, FUZZY, AND ADAPTIVE APPROACHES Edited by David A. White Donald A. Sorge \~1l ~ VAN NOSTRAND REINHOLD ~ New York Co,", 10 NEURAL NETWORKS, SYSTEM IDENTIFICATION, AND CONTROL IN THE CHEMICAL PROCESS INDUSTRIES Paull. Werbos NSF Program Director for Neuroengineering Co-Director for Emerging Technologies Initiation Thomas McAvoy and Ted Su Department of Chemical Engineering. University of Maryland 10.1. INTRODUCTION AND SUMMARY This chapter will discuss the application of artificial neural networks (ANNs) in the chemical process industries. with particular emphasis on new designs that could be useful in other applications (neural and nonneuraI) as well: .The use of simple ANNs as the system identification component of model-based predictive control (MPC), a widely used scheme that optimizes over time subject to constraints. .The use of "robust" or "parallel" training of ANNs used in system identification. In tests on real data from a wastewater treatment plant and an oil refinery, this has led to an orders-of -magnitude reduction in prediction error when compared with conventional maximum-likelihood ap- proaches. Earlier tests on nonneural implementations were also promising. There are deep theoretical lessons involved, significant to all forms of ANN adaptation and system identifica- tion. .Techniques for pruning ANNs (a task which has turned out to be crucial to ANN performance). techniques for using ANN-like techniques with other nonlinear specifications (for those who do not want to use ANNs as such) and techniques for using ANN models similar to nonlinear ARMA models or Kalman filtering systems-to be given in later sections. ANNs are already in use in real-world applications by many of the major chemical manufacturers.
    [Show full text]
  • Handbook of Intelligent Control
    HANDBOOK OF INTELLIGENT CONTROL NEURAL, FUZZY, AND ADAPTIVE APPROACHES Edited by David A. White Donald A. Sorge \~1l ~ VAN NOSTRAND REINHOLD ~ New York FOREWORD 1 This book is an outgrowth of discussions that got started in at least three workshops sponsored by the National Science Foundation (NSF): .A workshop on neurocontrol and aerospace applications held in October 1990, under joint sponsorship from McDonnell Douglas and the NSF programs in Dynamic Systems and Control and Neuroengineering .A workshop on intelligent control held in October 1990, under joint sponsorship from NSF and the Electric Power Research Institute, to scope out plans for a major new joint initiative in intelligent control involving a number of NSF programs .A workshop on neural networks in chemical processing, held at NSF in January-February 1991, sponsored by the NSF program in Chemical Reaction Processes The goal of this book is to provide an authoritative source for two kinds of information: (1) fundamental new designs, at the cutting edge of true intelligent control, as well as opportunities for future research to improve on these designs; (2) important real-world applications, including test problems that constitute a challenge to the entire control community. Included in this book are a seriesof realistic test problems, worked out through lengthy discussions between NASA, NetJroDyne, NSF, McDonnell Douglas, and Honeywell, which are more than just benchmarks for evaluating intelligent control designs. Anyone who contributes to solving these problems may well be playing a crucial role in making possible the future development of hypersonic vehicles and subsequently the economic human settlement of outer space.This book also emphasizes chemical process applications (capable of improving the environment as well as increasing profits), the manufacturing of high-qual- ity composite parts, and robotics.
    [Show full text]
  • Complex System and Intelligent Control: Theories and Applications
    Chen et al. / Front Inform Technol Electron Eng 2019 20(1):1-3 1 Frontiers of Information Technology & Electronic Engineering www.jzus.zju.edu.cn; engineering.cae.cn; www.springerlink.com ISSN 2095-9184 (print); ISSN 2095-9230 (online) E-mail: [email protected] Editorial: Complex system and intelligent control: theories and applications Jie CHEN†‡1,2, Ben M. CHEN3,4, Jian SUN1 1School of Automation, Beijing Institute of Technology, Beijing 100081, China 2Tongji University, Shanghai 200092, China 3Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong, China 4Department of Electrical and Computer Engineering, National University of Singapore, Singapore †E-mail: [email protected] https://doi.org/10.1631/FITEE.1910000 Complex systems are the systems that consist of and swarming intelligence. Intelligent control tries to a great many diverse and autonomous but interacting borrow ideas from the sciences of physics, mathe- and interdependent components whose aggregate matics, biology, neuroscience, and others to develop a behaviors are nonlinear. As phased by Aristotle, “the control system to manage a complex process in an whole is more than the sum of its parts;” properties of uncertain environment. Intelligent control is a kind of complex systems are not a simple summation of their interdisciplinary new technology, which integrates individual parts. the knowledges of control theory, computer science, Complex systems are widespread. Typical exam- artificial intelligence, information theory, and other ples of complex systems can be found in the human areas. Intelligent control has been shown to be effec- brain, flocking formation of migrating birds, power tive in controlling complex systems where traditional grid, transportation systems, autonomous vehicles, control methods can hardly perform well.
    [Show full text]
  • Анализ Данных В Науке И Технике Data-Driven Science and Engineering
    Стивен Л. Брантон, Дж. Натан Куц Анализ данных в науке и технике Data-Driven Science and Engineering Machine Learning, Dynamical Systems, and Control Steven L. Brunton, J. Nathan Kutz Анализ данных в науке и технике Машинное обучение, динамические системы и управление Стивен Л. Брантон, Дж. Натан Куц Москва, 2021 УДК 001.5, 004.6 ББК 20, 32.97 Б87 Брантон С. Л., Куц Дж. Н. Б87 Анализ данных в науке и технике / пер. с англ. А. А. Слинкина. – М.: ДМК Пресс, 2021. – 542 с.: ил. ISBN 978-5-97060-910-1 Открытия, сделанные на основе анализа данных, совершили революцию в мо- делировании, прогнозировании поведения и управлении сложными системами. В этой книге приводятся сведения из машинного обучения, инженерной математи- ки и математической физики с целью показать, как моделирование и управление динамическими системами сочетаются с современными методами науки о дан- ных. Рассказывается о многих достижениях в области научных расчетов, которые позволяют применять управляемые данными методы к изучению разнообразных сложных систем, таких как турбулентность, науки о мозге, климатология, эпиде- миология, финансы, робототехника и автономные системы. Книга рассчитана на интересующихся студентов старших курсов и аспирантов первого года обучения инженерных и физических специальностей, в ней рассмат­ ривается широкий круг тем и методов на уровне от введения до недавних работ. УДК 001.5, 004.6 ББК 20, 32.97 *** Russian language edition copyright © 2021 by DMK Press. All rights reserved. Все права защищены. Любая часть этой книги не может быть воспроизведена в ка- кой бы то ни было форме и какими бы то ни было средствами без письменного разрешения владельцев авторских прав. ISBN 978­1­108­42209­3 (англ.) © Steven L.
    [Show full text]
  • The IEEE Control Systems Society (CSS) Technical Committee on Intelligent Control (TCIC) Was Established in 1985 to Foster A
    The IEEE Control Systems Society (CSS) Technical Committee on Intelligent Control (TCIC) was established in 1985 to foster a community and to provide forums for the theoretical and practical considerations of intelligent control techniques and their application to devices and systems. From its inception through the turn of the millennia, rapid advancements established cornerstone techniques for incorporating intelligent control techniques within feedback control approaches. For example, fundamental contributions to our understanding of how to use Lyapunov-based analysis methods to inform the design of gradient and least-squares adaptive update laws and modular and composite adaptive control were established. Neural networks (NN) and fuzzy logic (FL) were transformed from computational tools used for pattern matching based on a finite set of training data to powerful function approximation tools that used backpropagation with adaptation through online state feedback. Iterative (ILC) and repetitive learning control (RLC) methods were developed and matured, and various methods were developed for improved transient performance during adaptation and simultaneous parameter identification. Refinements, generalizations, and the application of these methods are still on-going areas of research. The aforementioned intelligent control techniques have been successfully absorbed into mainstream control systems research. Adoption of intelligent control within control communities is evident from the number of conferences across several societies that
    [Show full text]
  • RECURRENT NEURAL NETWORKS Design and Applications
    RECURRENT NEURAL NETWORKS Design and Applications Edited by L.R. Medsker Departments of Physics and Computer Science and Information Systems American University Washington, D.C. L.C. Jain Knowledge-Based Intelligent Engineering Systems Centre Faculty of Information Technology Director/Founder, KES University of South Australia, Adelaide The Mawson Lakes, SA Australia CRC Press Boca Raton London New York Washington, D.C. PREFACE Recurrent neural networks have been an interesting and important part of neural network research during the 1990's. They have already been applied to a wide variety of problems involving time sequences of events and ordered data such as characters in words. Novel current uses range from motion detection and music synthesis to financial forecasting. This book is a summary of work on recurrent neural networks and is exemplary of current research ideas and challenges in this subfield of artificial neural network research and development. By sharing these perspectives, we hope to illuminate opportunities and encourage further work in this promising area. Two broad areas of importance in recurrent neural network research, the architectures and learning techniques, are addressed in every chapter. Architectures range from fully interconnected to partially connected networks, including recurrent multilayer feedforward. Learning is a critical issue and one of the primary advantages of neural networks. The added complexity of learning in recurrent networks has given rise to a variety of techniques and associated research projects. A goal is to design better algorithms that are both computationally efficient and simple to implement. Another broad division of work in recurrent neural networks, on which this book is structured, is the design perspective and application issues.
    [Show full text]
  • Intelligent Flapping Wing Control Reinforcement Learning for the Delfly M.W
    Intelligent Flapping Wing Control Reinforcement Learning for the DelFly M.W. Goedhart Technische Universiteit Delft Intelligent Flapping Wing Control Reinforcement Learning for the DelFly Master of Science Thesis For obtaining the degree of Master of Science in Aerospace Engineering at Delft University of Technology M.W. Goedhart June 7, 2017 Faculty of Aerospace Engineering Delft University of Technology ¢ Delft University Of Technology Department Of Control and Simulation Dated: June 7, 2017 Readers: Dr. ir. E. van Kampen Dr. ir. C. C. de Visser Dipl.-Ing. S. F.Armanini Dr. O. A. Sharpanskykh Dr. ir. Q. P.Chu Preface This reports concludes the work conducted for my thesis. In the graduation phase at the department of Control and Simulation at the faculty of Aerospace Engineering of Delft University of Technology, a phase of academic research is formalized in the course AE5310 Thesis Control and Operations. This report is intended to show the knowledge gained during the thesis phase. The main scientific contri- butions of this project are described in a scientific paper, which is included in this thesis. This is intended as a conference paper. It is aimed at readers with a background in intelligent aerospace control, and is therefore written at a more difficult level. It should be readable as a standalone document. For readers without previous knowledge of Reinforcement Learning control, a literature survey is sum- marized in this thesis. This should be sufficient to understand the paper. Such readers are advised to read Chapter6 to9 before continuing to the paper. I would like to thank dr. Erik-Jan van Kampen, dr.
    [Show full text]
  • Machine Learning in a Dynamic World" (Invited), M.M.Kokar Organizer, P Roc
    P. J. Antsaklis, "Learning in Control,” in Panel Discussion on "Machine Learning in a Dynamic World" (Invited), M.M.Kokar Organizer, P roc. o f t he 3 rd I EEE I ntern. S ymposium o n I ntelligent C ontro l , pp. 500- 507, Arlington, VA, Aug. 24-26, 1988. MACHINE LEARNING IN A DYNAMIC WORLD Panel Dmussion Miecsyslaw M. Kohr Panel O&m and Editor Northeastern Univdty Boston, Massachwtts Panelists: P. J. Atnsaklis, University of Notre Dame K. A. DeJong, George Mason University A. L. Meyrowitz, Office of Naval Research A. Meystel, Drexel University R. S. Michal~gi,George Mason University R. S. Sutton, GTE Laboratories Machine Learning was established as a research discipline in the lems while ignoring common-senseknowledge. How much of progresd 1970's and experienced a growth expansion in the 1980's. One of in controlling real-world systems can be made without taking into the roots of machine learning research was in Cybernetic Systems account all kinds of imprecise information? and Adaptive Control. Machine Learning has been significantly influenced by Artificial Intelligence, Cognitive Science, Computer In the following we present the views of the panelists. This pre- Science, and other disciplines; Machine Learning has developed its sentation is based on the written material submitted to the panel own research paradigms, methodologies, and a set of research ob- organizers by the panelists. The preeentation has been divided jectives different from those of control systems. In the meantime, a into four sections. The first section is devoted to the trends in the new field of Intelligent Control has emerged.
    [Show full text]
  • Classifying Intelligence in Machines: a Taxonomy of Intelligent Control †
    robotics Article Classifying Intelligence in Machines: A Taxonomy of Intelligent Control † Callum Wilson * , Francesco Marchetti , Marilena Di Carlo , Annalisa Riccardi and Edmondo Minisci Intelligent Computational Engineering Laboratory, Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK; [email protected] (F.M.); [email protected] (M.D.C.); [email protected] (A.R.); [email protected] (E.M.) * Correspondence: [email protected] † This paper is an extended version of our paper published in Wilson, C.; Marchetti, F.; Di Carlo, M.; Riccardi, A.; Minisci, E. Intelligent Control: A Taxonomy. In Proceedings of the 8th International Conference on Systems and Control (ICSC 2019), Marrakech, Morocco, 23–25 October 2019. Received: 30 June 2020; Accepted: 18 August 2020; Published: 21 August 2020 Abstract: The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications.
    [Show full text]
  • Identification and Predictive Control Using Recurrent Neural Networks
    International Master’s Thesis Identification and Predictive Control Using Recurrent Neural Networks Nima Mohajerin Technology Studies from the Department of Technology at Örebro University örebro 2012 Identification and Predictive Control Using Recurrent Neural Networks Studies from the Department of Technology at Örebro University Nima Mohajerin Identification and Predictive Control Using Recurrent Neural Networks Supervisor: Prof. Ivan Kalaykov Examiners: Dr. Dimitar Dimitrov Dr. Boyko Iliev © Nima Mohajerin, 2012 Title: Identification and Predictive Control Using Recurrent Neural Networks ISSN 1650-8580 Abstract In this thesis, a special class of Recurrent Neural Networks (RNN) is em- ployed for system identification and predictive control of time dependent sys- tems. Fundamental architectures and learning algorithms of RNNs are studied upon which a generalized architecture over a class of state-space represented networks is proposed and formulated. Levenberg-Marquardt (LM) learning algorithm is derived for this architecture and a number of enhancements are introduced. Furthermore, using this recurrent neural network as a system iden- tifier, a Model Predictive Controller (MPC) is established which solves the op- timization problem using an iterative approach based on the LM algorithm. Simulation results show that the new architecture accompanied by LM learn- ing algorithm outperforms some of existing methods. The third approach which utilizes the proposed method in on-line system identification enhances the iden- tification/control process even more. i Acknowledgements I would like to express my sincere gratitude to my supervisor, Professor Ivan Kalaykov, who trusted in me and gave me the opportunity to pursue my pas- sion in studying and conducting my research in Neural Networks. I hope I have fulfilled his expectations.
    [Show full text]
  • Energy Consumption Prediction Using Machine Learning; a Review
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 11 March 2019 doi:10.20944/preprints201903.0131.v1 Review Energy consumption prediction using machine learning; a review Amir Mosavi 1,2,3*, Abdullah Bahmani 1, 1 Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway; [email protected] 2 Kando Kalman Faculty of Electrical Engineering, Obuda University, Budapest; Hungary 3 School of the Built Environment, Oxford Brookes University, Oxford, UK Abstract: Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models. Keywords: energy consumption; prediction; machine learning models; deep learning models; artificial intelligence (AI); computational intelligence (CI); forecasting; soft computing (SC); 1. Introduction Energy consumption is among one of the essential topics of energy systems.
    [Show full text]
  • The Challenge of Intelligent Systems
    Proceedings of the 15th IEEE International Symposium on Intelligent Control (ISIC 2000) Rio, Patras, GREECE 17-19 July, 2000 THE CHALLENGE OF INTELLIGENT SYSTEMS ALEXANDER MEYSTEL†§, ELENA MESSINA§ †Drexel University, Philadelphia, PA, USA, [email protected] §National Institute of Standards and Technology, Gaithersburg, MD, USA [email protected] Abstract. This paper analyzes the meaning of intelligence within the domain of intelligent control. Because of historical reasons, there are varying and diverse definitions of what constitutes an intelligent control system. Due to the multidisciplinary nature of intelligent control, there are no good formal measures for what is an intelligent controller or for its performance. We argue that there are characteristics which can be used to categorize a controller as being intelligent. Furthermore, the measure of how intelligent a system is should take into account a holistic view of the system’s operations and design requirements. Beginning with the definition of intelligence characteristics and performance measures, we can move towards a science of intelligent systems for control. Key words. Intelligent control, intelligent systems, performance measures In this paper, we analyze the history of intelligent 1. INTRODUCTION control, its multidisciplinarity, and the higher level issues of “meta control” that characterize intelligent “Intelligent Control” is a commonly used term, systems. We argue that there are characteristics that applied to an amorphous set of tools, techniques, and can be found in common among most, if not all, approaches to implementing control systems that systems labeled as intelligent controllers. These have capabilities beyond those attainable through characteristics suggest espousing a broader view of classical control.
    [Show full text]