Adaptive Dynamic Programming with Applications in Optimal Control (eBook)

Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li (Autoren)

eBook Download: PDF

2017 | 1st ed. 2017
XXX, 594 Seiten
Springer International Publishing (Verlag)
978-3-319-50815-3 (ISBN)

Infos

This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors' work:

• renewable energy scheduling for smart power grids;

• coal gasification processes; and

• water-gas shift reactions.

Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.

Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame, Indiana, USA, in 1994. Dr. Liu was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the '100 Talents Program' by the Chinese Academy of Sciences in 2008. He has published 16 books. Dr. Liu was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems, from 2010 to 2015. Currently, he is an elected AdCom member of the IEEE Computational Intelligence Society, he is the Editor-in-Chief of Artificial Intelligence Review, and he serves as the Vice President of Asia-Pacific Neural Network Society. He was the General Chair of 2014 IEEE World Congress on Computational Intelligence and was the General Chair of 2016 World Congress on Intelligent Control and Automation. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008, and the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014. He is a Fellow of the IEEE and a Fellow of the International Neural Network Society.  

Qinglai Weie='font-family: 'Courier New';'> received the Ph.D. degree in control theory and control engineering, from the Northeastern University, Shenyang, China, in 2009. From 2009 to 2011, he was a postdoctoral fellow with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He is currently a Professor of the institute. Prof. Wei is an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Sciences, Neurocomputing, Optimal Control Applications and Methods, and Acta Automatica Sinica, and was an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems during 2014-2015. He was the organizing committee member of several international conferences. He was recipient of Asia Pacific Neural Networks Society (APNNS) young researcher award in 2016. He was a recipient of the Outstanding Paper Award of Acta Automatica Sinica in 2011 and Zhang Siying Outstanding Paper Award of Chinese Control and Decision Conference (CCDC) in 2015.

Ding Wang received the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2012. He is currently an Associate Professor with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He has been a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA, since 2015. His research interests include adaptive and learning systems, intelligent control, and neural networks. He has published over 70 journal and conference papers, and coauthored two monographs. He was the organizing committee member of several international conferences. He was recipient of the Excellent Doctoral Dissertation Award of Chinese Academy of Sciences in 2013. He serves as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing. He is a member of IEEE, Asia-Pacific Neural Network Society (APNNS), and CAA. 

Xiong Yang received the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2014. Dr. Yang was a recipient of the Excellent Award of Presidential Scholarship of Chinese Academy of Sciences in 2014. He was an Assistant Professor with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, from 2014 to 2016. He is currently an Associate Professor with School of Electrical Engineering and Automation, Tianjin University.

Hongliang Li received the Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences in 2015. Dr. Li was a Research Scientist with IBM Research - China, Beijing, from 2015 to 2016. He joined Tencent Inc., Shenzhen, China, in 2016. He has published more than 10 journal papers on adaptive dynamic programming and reinforcement learning.

Inhalt

Foreword 6
Series Editors’ Foreword 8
References 10
Preface 11
Acknowledgements 16
Contents 17
Abbreviations 24
Symbols 25
1 Overview of Adaptive Dynamic Programming 27
1.1 Introduction 27
1.2 Reinforcement Learning 29
1.3 Adaptive Dynamic Programming 33
1.3.1 Basic Forms of Adaptive Dynamic Programming 36
1.3.2 Iterative Adaptive Dynamic Programming 41
1.3.3 ADP for Continuous-Time Systems 44
1.3.4 Remarks 47
1.4 Related Books 48
1.5 About This Book 52
References 53
Part I Discrete-Time Systems 60
2 Value Iteration ADP for Discrete-Time Nonlinear Systems 61
2.1 Introduction 61
2.2 Optimal Control of Nonlinear Systems Using General Value Iteration 62
2.2.1 Convergence Analysis 64
2.2.2 Neural Network Implementation 72
2.2.3 Generalization to Optimal Tracking Control 76
2.2.4 Optimal Control of Systems with Constrained Inputs 80
2.2.5 Simulation Studies 83
2.3 Iterative ?-Adaptive Dynamic Programming Algorithm for Nonlinear Systems 91
2.3.1 Convergence Analysis 93
2.3.2 Optimality Analysis 101
2.3.3 Summary of Iterative ?-ADP Algorithm 104
2.3.4 Simulation Studies 107
2.4 Conclusions 111
References 111
3 Finite Approximation Error-Based Value Iteration ADP 115
3.1 Introduction 115
3.2 Iterative ?-ADP Algorithm with Finite Approximation Errors 116
3.2.1 Properties of the Iterative ADP Algorithm with Finite Approximation Errors 117
3.2.2 Neural Network Implementation 124
3.2.3 Simulation Study 128
3.3 Numerical Iterative ?-Adaptive Dynamic Programming 131
3.3.1 Derivation of the Numerical Iterative ?-ADP Algorithm 131
3.3.2 Properties of the Numerical Iterative ?-ADP Algorithm 135
3.3.3 Summary of the Numerical Iterative ?-ADP Algorithm 144
3.3.4 Simulation Study 145
3.4 General Value Iteration ADP Algorithm with Finite Approximation Errors 149
3.4.1 Derivation and Properties of the GVI Algorithm with Finite Approximation Errors 149
3.4.2 Designs of Convergence Criteria with Finite Approximation Errors 157
3.4.3 Simulation Study 164
3.5 Conclusions 171
References 171
4 Policy Iteration for Optimal Control of Discrete-Time Nonlinear Systems 174
4.1 Introduction 174
4.2 Policy Iteration Algorithm 175
4.2.1 Derivation of Policy Iteration Algorithm 176
4.2.2 Properties of Policy Iteration Algorithm 177
4.2.3 Initial Admissible Control Law 183
4.2.4 Summary of Policy Iteration ADP Algorithm 185
4.3 Numerical Simulation and Analysis 185
4.4 Conclusions 196
References 197
5 Generalized Policy Iteration ADP for Discrete-Time Nonlinear Systems 199
5.1 Introduction 199
5.2 Generalized Policy Iteration-Based Adaptive Dynamic Programming Algorithm 199
5.2.1 Derivation and Properties of the GPI Algorithm 201
5.2.2 GPI Algorithm and Relaxation of Initial Conditions 210
5.2.3 Simulation Studies 214
5.3 Discrete-Time GPI with General Initial Value Functions 221
5.3.1 Derivation and Properties of the GPI Algorithm 221
5.3.2 Relaxations of the Convergence Criterion and Summary of the GPI Algorithm 233
5.3.3 Simulation Studies 237
5.4 Conclusions 243
References 243
6 Error Bounds of Adaptive Dynamic Programming Algorithms 244
6.1 Introduction 244
6.2 Error Bounds of ADP Algorithms for Undiscounted Optimal Control Problems 245
6.2.1 Problem Formulation 245
6.2.2 Approximate Value Iteration 247
6.2.3 Approximate Policy Iteration 252
6.2.4 Approximate Optimistic Policy Iteration 258
6.2.5 Neural Network Implementation 262
6.2.6 Simulation Study 264
6.3 Error Bounds of Q-Function for Discounted Optimal Control Problems 268
6.3.1 Problem Formulation 268
6.3.2 Policy Iteration Under Ideal Conditions 270
6.3.3 Error Bound for Approximate Policy Iteration 275
6.3.4 Neural Network Implementation 278
6.3.5 Simulation Study 280
6.4 Conclusions 283
References 284
Part II Continuous-Time Systems 286
7 Online Optimal Control of Continuous-Time Affine Nonlinear Systems 287
7.1 Introduction 287
7.2 Online Optimal Control of Partially Unknown Affine Nonlinear Systems 287
7.2.1 Identifier--Critic Architecture for Solving HJB Equation 289
7.2.2 Stability Analysis of Closed-Loop System 301
7.2.3 Simulation Study 306
7.3 Online Optimal Control of Affine Nonlinear Systems with Constrained Inputs 311
7.3.1 Solving HJB Equation via Critic Architecture 314
7.3.2 Stability Analysis of Closed-Loop System with Constrained Inputs 318
7.3.3 Simulation Study 322
7.4 Conclusions 325
References 326
8 Optimal Control of Unknown Continuous-Time Nonaffine Nonlinear Systems 328
8.1 Introduction 328
8.2 Optimal Control of Unknown Nonaffine Nonlinear Systems with Constrained Inputs 329
8.2.1 Identifier Design via Dynamic Neural Networks 330
8.2.2 Actor--Critic Architecture for Solving HJB Equation 335
8.2.3 Stability Analysis of Closed-Loop System 337
8.2.4 Simulation Study 342
8.3 Optimal Output Regulation of Unknown Nonaffine Nonlinear Systems 346
8.3.1 Neural Network Observer 347
8.3.2 Observer-Based Optimal Control Scheme Using Critic Network 352
8.3.3 Stability Analysis of Closed-Loop System 356
8.3.4 Simulation Study 359
8.4 Conclusions 362
References 362
9 Robust and Optimal Guaranteed Cost Control of Continuous-Time Nonlinear Systems 364
9.1 Introduction 364
9.2 Robust Control of Uncertain Nonlinear Systems 365
9.2.1 Equivalence Analysis and Problem Transformation 367
9.2.2 Online Algorithm and Neural Network Implementation 369
9.2.3 Stability Analysis of Closed-Loop System 372
9.2.4 Simulation Study 375
9.3 Optimal Guaranteed Cost Control of Uncertain Nonlinear Systems 379
9.3.1 Optimal Guaranteed Cost Controller Design 381
9.3.2 Online Solution of Transformed Optimal Control Problem 387
9.3.3 Stability Analysis of Closed-Loop System 392
9.3.4 Simulation Studies 397
9.4 Conclusions 402
References 403
10 Decentralized Control of Continuous-Time Interconnected Nonlinear Systems 406
10.1 Introduction 406
10.2 Decentralized Control of Interconnected Nonlinear Systems 407
10.2.1 Decentralized Stabilization via Optimal Control Approach 408
10.2.2 Optimal Controller Design of Isolated Subsystems 413
10.2.3 Generalization to Model-Free Decentralized Control 419
10.2.4 Simulation Studies 423
10.3 Conclusions 433
References 433
11 Learning Algorithms for Differential Games of Continuous-Time Systems 435
11.1 Introduction 435
11.2 Integral Policy Iteration for Two-Player Zero-Sum Games 436
11.2.1 Derivation of Integral Policy Iteration 438
11.2.2 Convergence Analysis 441
11.2.3 Neural Network Implementation 443
11.2.4 Simulation Studies 446
11.3 Iterative Adaptive Dynamic Programming for Multi-player Zero-Sum Games 449
11.3.1 Derivation of the Iterative ADP Algorithm 451
11.3.2 Properties 456
11.3.3 Neural Network Implementation 462
11.3.4 Simulation Studies 469
11.4 Synchronous Approximate Optimal Learning for Multi-player Nonzero-Sum Games 477
11.4.1 Derivation and Convergence Analysis 478
11.4.2 Neural Network Implementation 482
11.4.3 Simulation Study 491
11.5 Conclusions 496
References 496
Part III Applications 499
12 Adaptive Dynamic Programming for Optimal Residential Energy Management 500
12.1 Introduction 500
12.2 A Self-learning Scheme for Residential Energy System Control and Management 501
12.2.1 The ADHDP Method 505
12.2.2 A Self-learning Scheme for Residential Energy System 506
12.2.3 Simulation Study 509
12.3 A Novel Dual Iterative Q-Learning Method for Optimal Battery Management 513
12.3.1 Problem Formulation 513
12.3.2 Dual Iterative Q-Learning Algorithm 514
12.3.3 Neural Network Implementation 520
12.3.4 Numerical Analysis 523
12.4 Multi-battery Optimal Coordination Control for Residential Energy Systems 530
12.4.1 Distributed Iterative ADP Algorithm 532
12.4.2 Numerical Analysis 544
12.5 Conclusions 550
References 550
13 Adaptive Dynamic Programming for Optimal Control of Coal Gasification Process 553
13.1 Introduction 553
13.2 Data-Based Modeling and Properties 554
13.2.1 Description of Coal Gasification Process and Control Systems 554
13.2.2 Data-Based Process Modeling and Properties 556
13.3 Design and Implementation of Optimal Tracking Control 562
13.3.1 Optimal Tracking Controller Design by Iterative ADP Algorithm Under System and Iteration Errors 562
13.3.2 Neural Network Implementation 570
13.4 Numerical Analysis 573
13.5 Conclusions 584
References 585
14 Data-Based Neuro-Optimal Temperature Control of Water Gas Shift Reaction 586
14.1 Introduction 586
14.2 System Description and Data-Based Modeling 587
14.2.1 Water Gas Shift Reaction 587
14.2.2 Data-Based Modeling and Properties 588
14.3 Design of Neuro-Optimal Temperature Controller 590
14.3.1 System Transformation 590
14.3.2 Derivation of Stable Iterative ADP Algorithm 591
14.3.3 Properties of Stable Iterative ADP Algorithm with Approximation Errors and Disturbances 593
14.4 Neural Network Implementation for the Optimal Tracking Control Scheme 597
14.5 Numerical Analysis 600
14.6 Conclusions 604
References 604
Index 606

Produkt-Details

• Erscheint lt. Verlag: 4.1.2017
• Reihe/Serie: Advances in Industrial Control
• Reihe/Serie: Advances in Industrial Control
• Zusatzinfo: XXX, 594 p. 203 illus., 175 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Naturwissenschaften ► Physik / Astronomie
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: ADP Approach • Approximate Dynamic Programming • Deterministic Systems • Intelligent Control • Learning Control • Neural networks • Neuro-dynamic Programming • optimal control • Policy Iteration • Reinforcement Learning • Sub-optimal Control
• ISBN-10: 3-319-50815-6 / 3319508156
• ISBN-13: 978-3-319-50815-3 / 9783319508153