: Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
: Adaptive Dynamic Programming with Applications in Optimal Control
: Springer-Verlag
: 9783319508153
: Advances in Industrial Control
: 1
: CHF 208.90
:
: Elektronik, Elektrotechnik, Nachrichtentechnik
: English
: 609
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF

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:

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• 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.

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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.  

e='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.

Honglian 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.

Foreword6
Series Editors’ Foreword8
References10
Preface11
Acknowledgements16
Contents17
Abbreviations24
Symbols25
1 Overview of Adaptive Dynamic Programming27
1.1 Introduction27
1.2 Reinforcement Learning29
1.3 Adaptive Dynamic Programming33
1.3.1 Basic Forms of Adaptive Dynamic Programming36
1.3.2 Iterative Adaptive Dynamic Programming41
1.3.3 ADP for Continuous-Time Systems44
1.3.4 Remarks47
1.4 Related Books48
1.5 About This Book52
References53
Part I Discrete-Time Systems60
2 Value Iteration ADP for Discrete-Time Nonlinear Systems61
2.1 Introduction61
2.2 Optimal Control of Nonlinear Systems Using General Value Iteration62
2.2.1 Convergence Analysis64
2.2.2 Neural Network Implementation72
2.2.3 Generalization to Optimal Tracking Control76
2.2.4 Optimal Control of Systems with Constrained Inputs80
2.2.5 Simulation Studies83
2.3 Iterative ?-Adaptive Dynamic Programming Algorithm for Nonlinear Systems91
2.3.1 Convergence Analysis93
2.3.2 Optimality Analysis101
2.3.3 Summary of Iterative ?-ADP Algorithm104
2.3.4 Simulation Studies107
2.4 Conclusions111
References111
3 Finite Approximation Error-Based Value Iteration ADP115
3.1 Introduction115
3.2 Iterative ?-ADP Algorithm with Finite Approximation Errors116
3.2.1 Properties of the Iterative ADP Algorithm with Finite Approximation Errors117
3.2.2 Neural Network Implementation124
3.2.3 Simulation Study128
3.3 Numerical Iterative ?-Adaptive Dynamic Programming131
3.3.1 Derivation of the Numerical Iterative ?-ADP Algorithm131
3.3.2 Properties of the Numerical Iterative ?-ADP Algorithm135
3.3.3 Summary of the Numerical Iterative ?-ADP Algorithm144
3.3.4 Simulation Study145
3.4 General Value Iteration ADP Algorithm with Finite Approximation Errors149
3.4.1 Derivation and Properties of the GVI Algorithm with Finite Approximation Errors149
3.4.2 Designs of Convergence Criteria with Finite Approximation Errors157
3.4.3 Simulation Study164
3.5 Conclusions171
References171
4 Policy Iteration for Optimal Control of Discrete-Time Nonlinear Syste