DOCSLIB.ORG

Complex System and Intelligent Control: Theories and Applications

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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. social networks, and communication networks. In this context, the Chinese Academy of Engi- Complex systems have some distinct properties, neering (CAE) organized a special issue on complex such as highly nonlinear dynamics, emergence, ad- system and intelligent control in Frontiers of Infor- aptation, and self-organization, which are difficult to mation Technology and Electronic Engineering. This model precisely. Such properties lead to difficulties in special issue aims to promote research on complex understanding the behaviors of complex systems, to systems and intelligent control and reflect the most model them accurately, to control them, and to make recent advances, with emphasis on both theories and them work in a specific way we desire. applications. After rigorous review process, 11 papers There is no generally agreed definition of intel- by researchers worldwide have been selected for this ligent control. Generally speaking, intelligent control special issue, including one survey paper and 10 re- is a class of control methods that use artificial intel- search papers. ligence techniques, such as fuzzy logic, neural net- Electric power grid as a typical example of works, evolutionary computation, machine learning, complex systems has brought a great change in our daily lives. An accurate, fast, and robust power sys- ‡ Corresponding author tem state estimation is undoubtedly significant. Gang © Zhejiang University and Springer-Verlag GmbH Germany, part of WANG et al. gave a comprehensive review on some Springer Nature 2019 2 Chen et al. / Front Inform Technol Electron Eng 2019 20(1):1-3 of the recent advances in power system state estima- ceived much attention since their wide applications in tion with an emphasis on the solvers that can effi- mobile sensor networks, formation of unmanned ciently obtain optimal or near-optimal solutions to flight vehicles, smart grids, etc. Formation control is nonconvex state estimation tasks. Some directions for an important topic in cooperative control for multi- future research were also discussed. agent systems. Mao-peng RAN et al. considered the Motion coverage is an important real-world ap- time-varying formation tracking problem for multi- plication for mobile robots. To deal with the multi- agent systems subject to unknown nonlinear dynam- robot coverage motion planning problem, Guan-qiang ics and external disturbances. An extended state ob- GAO and Bin XIN proposed an auction-based span- server (ESO) was designed to estimate the total un- ning tree coverage (A-STC) algorithm. Compared certainty of the system. An ESO-based time-varying with existing methods, this algorithm is effective, formation tracking protocol was proposed. Effec- time-efficient, and complete, especially in large tiveness of the theoretical results was demonstrated complex configuration spaces. by application to the target enclosing problem of a A snake robot is a biomorphic robot to imitate group of unmanned aerial vehicles (UAVs). Mao-bin snake’s morphology. To make a snake robot as agile LU and Lu LIU studied the leader-following con- as a biological snake, locomotion generation and sensus problem of a class of heterogeneous sec- body motion control are two important issues. ond-order nonlinear multi-agent systems subject to Wenjuan OUYANG et al. proposed a biomimetic disturbances. A class of distributed control laws that control approach for steering a snake robot. The depend on the relative state of the system were pro- proposed method includes an artificial central pattern posed to solve the problem. generator, a cerebellar model articulation controller, In recent years, UAVs have been widely applied and a proportional-derivative controller. Their simu- in many fields, such as reconnaissance, search and lations and experiments demonstrated good perfor- rescue, surveillance, environment monitoring, large- mance and adaptability of the method. accident investigation, parcel delivery, and agricul- Ning-shi YAO et al. presented an autonomous ture service. Among all the UAVs, the quadrotor robotic blimp equipped with only one monocular platform is the most popular one. Control and navi- camera, named Georgia Tech Miniature Autonomous gation of quadrotor UAV has received much attention. Blimp (GT-MAB). It was designed to support human Yu-jiang ZHONG et al. presented a reliable active robot interaction experiments in an indoor space. The fault-tolerant tracking control method for a quadrotor blimp is capable of effectively detecting the face of a UAV with model uncertainties and actuator faults. A human subject, following the human, and recognizing radial basis function neural network was introduced hand gestures by employing a deep neural network to estimate the model uncertainties online, modify the based algorithm. Their experimental results demon- reference model adaptively, and incorporate it into the strated that GT-MAB has reliable human-detection model reference adaptive control scheme. To deal and human-following capabilities. with actuator faults, a fault detection and diagnosis Execution control is a critical task of robot ar- estimator and a fault compensation term were intro- chitecture design, and has important influence on the duced into the control law. Shu-peng LAI et al. pro- quality of the final system. Martin MOLINA et al. posed a computationally efficient safe flying corridor presented a general method for execution control. The navigation method for a quadrotor UAV. This method proposed method was integrated in the Aerostack can generate a jerk-limited trajectory within a safe open-source framework. Their experiments demon- corridor. Unlike the existing methods, the proposed strated the validity, feasibility, and effectiveness of method can generate a smooth non-stop trajectory the proposed method. with safety guarantee but does not require the vehicle In recent years, multi-agent systems have re- stop at each intersected point of the connected boxes. Chen et al. / Front Inform Technol Electron Eng 2019 20(1):1-3 3 A flywheel suspended on active magnetic bear- Prof. Jie CHEN received BS, MS, and ings as a kind of complex system has been found to PhD degrees in control theory and control engineering from the Beijing have many applications in energy storage devices. Institute of Technology, Beijing, China, However, the controller design problem for a in 1986, 1993, and 2001, respectively. flywheel system is still challenging. Xujun LYU et al. He is currently the President of Tongji applied the characteristic model based all-coefficient University, China. He is an Academi- adaptive control method to active magnetic bearings cian of the Chinese Academy of Engi- neering and IEEE Fellow. His current suspended flywheel systems. An extensive simulation research interests include intelligent control and decision in showed that the characteristic model based all- complex systems, multi-agent systems, and optimization coefficient adaptive control method has considerable methods. robustness with respect to plant uncertainties, exter- Prof. Ben M. CHEN received the BS nal disturbances, and time delay. degree in computer science from Xia- Ze-zhi TANG et al. investigated the disturbance men University, China in 1983, MS rejection problem of active magnetic bearing systems. degree in electrical engineering from A disturbance observer based iterative learning con- Gonzaga University, USA in 1988, and trol strategy that combines the extended state ob- PhD degree in electrical and computer engineering from Washington State server with a classic iterative learning control law was University, USA in 1991. He is cur- proposed. Their simulation and comparison con- rently a
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]
  • 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]
  • 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.
    [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]
  • 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]
  • 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]
  • Fuzzy Control Systems - Jens Jäkel, Ralf Mikut and Georg Bretthauer
    CONTROL SYSTEMS, ROBOTICS AND AUTOMATION – Vol. XVII - Fuzzy Control Systems - Jens Jäkel, Ralf Mikut and Georg Bretthauer FUZZY CONTROL SYSTEMS Jens Jäkel, Ralf Mikut and Georg Bretthauer Institute of Applied Computer Science, Forschungszentrum Karlsruhe GmbH, Germany Keywords: Adaptation, defuzzification, expert knowledge, fuzzification, fuzzy control, fuzzy controller design, fuzzy control structures, fuzzy logic, fuzzy model, fuzzy operator, fuzzy Petri net, fuzzy set, fuzzy system, fuzzy system analysis, implementation, inference, intelligent control, knowledge-based system, linear system, linguistic term, linguistic variable, Mamdani-type fuzzy system, membership function, nonlinear system, rule base, Takagi-Sugeno-type fuzzy system, supervision Contents 1. Introduction 2. Fuzzy Control – A Simple Example 2.1. Example 2.2. Fuzzy Sets, Linguistic Variables and Fuzzy IF-THEN Rules 2.3. Fuzzification - From Measurements to a Fuzzy Representation of the Input Situation 2.4. Inference - From a Fuzzy Input Representation to a Fuzzy Decision 2.5. Defuzzification - From a Fuzzy Decision to a Real Decision 3. Fuzzy Logic-related Issues in Fuzzy Control 3.1. Fuzzy Sets and Operations 3.2. Types of Rule-based Fuzzy Systems 3.3. Information Processing in Fuzzy Systems 4. Control Issues in Fuzzy Control 4.1. Structures in Fuzzy Control 4.2. Design of Fuzzy Control Systems 4.3. Analysis of Fuzzy Control Systems 4.4. Applications 4.5. Hardware and Software for Fuzzy Control Systems 4.6. Further Concepts Based on Fuzzy Sets Applied to Control Systems 5. Conclusions Glossary BibliographyUNESCO – EOLSS Biographical Sketches Summary SAMPLE CHAPTERS This chapter presents a perspective of fuzzy control systems. Fuzzy control is a form of intelligent control characterized by the use of expert knowledge on the control strategy and/or the behavior of the controlled plant.
    [Show full text]
  • Advances in Reinforcement Learning and Their Implications for Intelligent Control
    Advances in Reinforcement Learning and Their Implications for Intelligent Control Steven D. Whitehead*, Richard S. Suttoni and Dana H. Ballard* 1 Introduction 0 Robust under Incomplete and Uncertain Domain Knowledge: An intelligent control sys- What is an intelligent control system, and how will we tem must not depend upon a complete and accu- know one when we see it? This question is hard to rate domain model. Even for relatively simple, answer definitively, but intuitively what distinguishes narrow domains, it is extremely difficult to build intelligent control from more mundane control is the models that are complete and accurate [ShaSO]. complexity of the task and the ability of the control If control depends upon a domain model, it must system to deal with uncertain and changing conditions. suffice that the model be incomplete and inaccu- A list of the definitive properties of an intelligent con- rate. If domain knowledge is available, then the trol system might include some of the following: system should be capable of exploiting it, but it should not be a prerequisite for intelligent con- 0 Effective: The first requirement of any control system, intelligent or not, is that it generate con- trol. A system that uses a domain model that is trol actions that adequately control the plant (en- learned incrementally through experience is to be vironment). The adverb adequately is intention- preferred over one that relies upon a complete a ally imprecise since effective control rarely means priori model. optimal, but more often means sufficient for the Perceptual Feasibility: The information pro- purposes of the task.
    [Show full text]