neural systems for control

Neural Systems for Control 1 Omid M. Omidvar and David L. Elliott, Editors February, 1997 1 This the complete book (but with different pagination) Neural Systems for Control,O.M.Omidvar and D. L. Elliott, editors, Copyright 1997 by Academic Press, ISBN: 0125264305 and is posted with permission from Elsevier. http://www.isr.umd.edu/∼delliott/NeuralSystemsForControl.pdf ii Contents Contributors vii Preface xi 1Introduction: Neural Networks and Automatic Control 1 1 Control Systems 1 2WhatisaNeural Network? 3 2Reinforcement Learning 7 1Introduction 7 2Non-Associative Reinforcement Learning 8 3Associative Reinforcement Learning 12 4 Sequential Reinforcement Learning 20 5 Conclusion 26 6References 27 3Neurocontrol in Sequence Recognition 31 1Introduction 31 2HMM Source Models 32 3Recognition: Finding the Best Hidden Sequence 33 4 Controlled Sequence Recognition 34 5ASequential Event Dynamic Neural Network . 42 6Neurocontrol in sequence recognition 49 7 Observations and Speculations 52 8References 56 4ALearning Sensorimotor Map of Arm Movements: a Step Toward Biological Arm Control 61 1Introduction 61 2Methods 63 3Simulation Results 71 4Discussion 85 5References 86 5Neuronal Modeling of the Baroreceptor Reflex with Appli- cations in Process Modeling and Control 89 1Motivation 89 2The Baroreceptor Vagal Reflex 90 iv 3ANeuronal Model of the Baroreflex 95 4Parallel Control Structures in the Baroreflex . . 103 5Neural Computational Mechanisms for Process Modeling . . 116 6 Conclusionsand Future Work 120 7References 123 6Identification of Nonlinear Dynamical Systems Using Neu- ral Networks 127 1Introduction 127 2Mathematical Preliminaries 129 3State space models for identification 136 4Identification using Input-Output Models . . . 139 5 Conclusion 150 6References 153 7Neural Network Control of Robot Arms and Nonlinear Systems 157 1Introduction 157 2Background in Neural Networks, Stability, and Passivity . . 159 3Dynamics of Rigid Robot Arms 162 4NNController for Robot Arms 164 5Passivity andStructurePropertiesofthe NN 177 6Neural Networksfor Control of NonlinearSystems 183 7Neural Network Control with Discrete-Time Tuning 188 8 Conclusion 203 9References 203 8Neural Networks for Intelligent Sensors and Control — Practical Issues and Some Solutions 207 1Introduction 207 2CharacteristicsofProcessData 209 3Data Pre-processing 211 4Variable Selection 213 5Effect of Collinearity on Neural Network Training 215 6Integrating Neural Nets with Statistical Approaches 218 7Application to a Refinery Process 221 8 Conclusions and Recommendations 222 9References 223 9Approximation of Time–Optimal Control for an Industrial Production Plant with General Regression Neural Net- work 227 1Introduction 227 2Description of the Plant 228 3Model of the Induction Motor Drive 230 v 4General Regression Neural Network 231 5 Control Concept 234 6 Conclusion 241 7References 242 10 Neuro-Control Design: Optimization Aspects 251 1Introduction 251 2Neuro-Control Systems 252 3 Optimization Aspects 264 4PNC Design and Evolutionary Algorithm . . . 268 5 Conclusions 270 6References 272 11 Reconfigurable Neural Control in Precision Space Struc- tural Platforms 279 1 Connectionist Learning System 279 2Reconfigurable Control 282 3Adaptive Time-Delay Radial Basis Function Network 284 4Eigenstructure Bidirectional Associative Memory 287 5Fault Detection and Identification 291 6Simulation Studies 293 7 Conclusion 297 8References 297 12 Neural Approximations for Finite- and Infinite-Horizon Op- timal Control 307 1Introduction 307 2Statement of the finite–horizon optimal control problem . . 309 3Reduction of the functional optimization Problem 1 to a nonlinear programming problem 310 4Approximating properties of the neural control law 313 5Solution of the nonlinear programming problem by the gra- dientmethod 316 6Simulation results 319 7Statements of the infinite-horizon optimal control problem and of its receding-horizon approximation . . . 324 8Stabilizing properties of the receding–horizon regulator . . . 327 9The neural approximation for the receding–horizon regulator 330 10 A gradient algorithm for deriving the RH neural regulator and simulation results 333 11 Conclusions 335 12 References 337 Index 341 vi Contributors to this volume • Andrew G. Barto * Department of Computer Science University of Massachusetts Amherst MA 01003, USA E-mail: barto@cs.umass.edu • William J. Byrne * Center for Language and Speech Processing, Barton Hall Johns Hopkins University Baltimore MD 21218, USA E-mail: byrne@cspjhu.ece.jhu.edu • Sungzoon Cho Department of Computer Science and Engineering * POSTECH Information Research Laboratories Pohang University of Science and Technology San 31 Hyojadong Pohang, Kyungbook 790-784, South Korea E-mail: zoon@zoon.postech.ac.kr • Francis J. Doyle III * School of Chemical Engineering Purdue University West Lafayette, IN 47907-1283, USA E-mail: fdoyle@ecn.purdue.edu • David L. Elliott Institute for Systems Research University of Maryland College Park, MD 20742, USA E-mail: delliott@isr.umd.edu • Michael A.Henson Department of Chemical Engineering Louisiana State University Baton Rouge, LA 70803-7303, USA E-mail: henson@nlc.che.lsu.edu • S. Jagannathan Controls Research, Caterpillar, Inc. viii Tech. Ctr. Bldg. “E“, M/S 855 14009 Old Galena Rd. Mossville, IL 61552, USA E-mail: saranj@cat.com • Min Jang * Department of Computer Science and Engineering POSTECH Information Research Laboratories Pohang University of Science and Technology San 31 Hyojadong Pohang, Kyungbook 790-784, South Korea E-mail: jmin@zoon.postech.ac.kr • Asriel U. Levin * Wells Fargo Nikko Investment Advisors, Advanced Strategies and Research Group 45 Fremont Street San Francisco, CA 94105, USA E-mail: asriel.levin@bglobal.com • Kumpati S. Narendra Center for Systems Science Department of Electrical Engineering Yale University New Haven, CT 06520, USA E-mail: Narendra@koshy.eng.yale.edu • Babatunde A. Ogunnaike Neural Computation Program, Strategic Process Technology Group E. I. Dupont de Nemours and Company Wilmington, DE 19880-0101, USA E-mail: ogunnaike@esspt0.dnet.dupont.com • Omid M. Omidvar Computer Science Department University of the District of Columbia Washington, DC 20008, USA E-mail: oomidvar@udcvax.bitnet • Thomas Parisini * Department of Electrical, Electronic and Computer Engineering DEEI–University of Trieste, Via Valerio 10, 34175 Trieste, Italy E-mail: thomas@dist.dist.unige.it • S. Joe Qin * Department of Chemical Engineering, Campus Mail Code C0400 University of Texas 0. Contributors ix Austin, TX 78712, USA E-mail: qin@che.utexas.edu • James A. Reggia Department of Computer Science, Department of Neurology, and Institute for Advanced Computer Studies University of Maryland College Park, MD 20742, USA E-mail: reggia@avion.cs.umd.edu • Ilya Rybak Neural Computation Program, Strategic Process Technology Group E. I. Dupont de Nemours and Company Wilmington, DE 19880-0101, USA E-mail: rybak@eplrx7.es.dupont.com • Tariq Samad Honeywell Technology Center Honeywell Inc. 3660 Technology Drive, MN65-2600 Minneapolis, MN 55418, USA E-mail: samad@htc.honeywell.com • Clemens Sch¨affner * Siemens AG Corporate Research and Development, ZFE T SN 4 Otto–Hahn–Ring 6 D–81730 Munich, Germany E-mail: Clemens.Schaeffner@zfe.siemens.de • Dierk Schr¨oder Institute for Electrical Drives Technical University of Munich Arcisstrasse 21, D – 80333 Munich, Germany E-mail: eat@e–technik.tu–muenchen.de • James A. Schwaber Neural Computation Program, Strategic Process Technology Group E. I. Dupont de Nemours and Company Wilmington, DE 19880-0101, USA E-mail: schwaber@eplrx7.es.dupont.com • Shihab A. Shamma Electrical Engineering Department and the Institute for Systems Re- search University of Maryland College Park, MD 20742, USA E-mail: sas@isr.umd.edu x • H. TedSu* Honeywell Technology Center Honeywell Inc. 3660 Technology Drive, MN65-2600 Minneapolis, MN 55418, USA E-mail: tedsu@htc.honeywell.com • Gary G. Yen * USAF Phillips Laboratory, Structures and Controls Division 3550 Aberdeen Avenue, S.E. Kirtland AFB, NM 8711, USA7 E-mail: yeng@plk.af.mil • Aydin Ye¸sildirek Measurement and Control Engineering Research Center College of Engineering Idaho State University Pocatello, ID 83209-806, USA0 E-mail: yesiaydi@fs.isu.edu • Riccardo Zoppoli Department of Communications, Computer and System Sciences University of Genoa, Via Opera Pia 11A 16145 Genova, Italy E-mail: rzop@dist.unige.it * Corresponding Author [...]... are shown in Chapter 9 The motivation for control system design is often to optimize a cost, such as the energy used or the time taken for a control action Control designed for minimum cost is called optimal control The problem of approximating optimal control in a practical way can be attacked with neural network methods, as in Chapter 11; its authors, wellknown control theorists, use the “receding-horizon”... for research or application The authors’ addresses are given in the Contributors list; their work represents both academic and industrial thinking This book is intended for a wide audience— those professionally involved in neural network research, such as lecturers and primary investigators in neural computing, neural modeling, neural learning, neural memory, and neurocomputers Neural Networks in Control. .. artificial neural systems directly applicable to control or making use of modern control theory The papers herein were refereed; we are grateful to those anonymous referees for their patient help David L Elliott, University of Omid M Omidvar, University of Maryland, College Park the District of Columbia July 1996 xii 1 Introduction: Neural Networks and Automatic Control David L Elliott 1 Control Systems. .. Chapters were obtained by an open call for papers on the InterNet and by invitation The topics requested included mathematical foundations; biological control architectures; applications of neural network control methods (neurocontrol) in high technology, process control, and manufacturing; reinforcement learning; and neural network approximations to optimal control The responses included leading edge... functional evaluations needed to provide control action The weight adjustment is often performed off-line, with historical data; provision for online adjustment or even for online learning, as some of the chapters describe, can permit the controller to adapt to a changing plant and environment As cheaper and faster neural hardware develops, it becomes important for the control engineer to anticipate where... Pauline Tang has my thanks for her constant encouragement and help in this project 2 Reinforcement Learning Andrew G Barto ABSTRACT Reinforcement learning refers to ways of improving performance through trial-and-error experience Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage... difficult to precisely define a class of reinforcement learning algorithms 4 Sequential Reinforcement Learning Sequential reinforcement requires improving the long-term consequences of an action, or of a strategy for performing actions, in addition to short-term consequences In these problems, it can make sense to forego short-term performance in order to achieve better performance over the long-term Tasks having... which was written by a team representing control engineering, chemical engineering and human physiology, examines the workings of 1 Introduction 3 blood pressure control (the vagal baroreceptor reflex) and shows how to mimic this control system for chemical process applications 2 What is a Neural Network? The neural networks” referred to in this book are a artificial neural networks, which are a way of using... reinforcement signal stimulus patterns actions Network FIGURE 4 A Network of Associative Reinforcement Units The reinforcement signal is broadcast to the all the units an associative reinforcement learning task, all the units are AR−P units, to which the reinforcement signal is uniformly broadcast (Figure 4) The units exhibit a kind of statistical cooperation in trying to increase their common reinforcement... with neural networks, automatic control problems are good industrial applications and have a dynamic or evolutionary nature lacking in static pattern-recognition; control ideas are also prevalent in the study of the natural neural networks found in animals and human beings If you are interested in the practice and theory of control, artificial neural networks offer a way to synthesize nonlinear controllers, . Elsevier. http://www.isr.umd.edu/∼delliott/NeuralSystemsForControl.pdf ii Contents Contributors vii Preface xi 1Introduction: Neural Networks and Automatic Control 1 1 Control Systems 1 2WhatisaNeural Network? 3 2Reinforcement. Neural Systems for Control 1 Omid M. Omidvar and David L. Elliott, Editors February, 1997 1 This the complete book (but with different pagination) Neural Systems for Control, O.M.Omidvar. Robot Arms 162 4NNController for Robot Arms 164 5Passivity andStructurePropertiesofthe NN 177 6Neural Networksfor Control of NonlinearSystems 183 7Neural Network Control with Discrete-Time Tuning