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© 2017

Adaptive Dynamic Programming with Applications in Optimal Control

  • Demonstrates the power of adaptive dynamic programming in giving a uniform treatment of affine and nonaffine nonlinear systems including regulator and tracking control

  • Demonstrates the flexibility of adaptive dynamic programming, extending it to various fields of control theory

  • Shows the reader how to bring the theoretical demonstrations into the real world with three application examples

Book

Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages i-xxx
  2. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
    Pages 1-33
  3. Discrete-Time Systems

    1. Front Matter
      Pages 35-35
    2. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 37-90
    3. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 91-149
    4. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 151-175
    5. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 177-221
    6. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 223-264
  4. Continuous-Time Systems

    1. Front Matter
      Pages 265-265
    2. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 267-307
    3. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 309-344
    4. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 345-386
    5. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 387-415
    6. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 417-480
  5. Applications

    1. Front Matter
      Pages 481-481
    2. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 483-535
    3. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 537-569
    4. Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
      Pages 571-590
  6. Back Matter
    Pages 591-594

About this book

Introduction

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.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Keywords

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

Authors and affiliations

  1. 1.University of Science and Technology BeijingBeijingChina
  2. 2.Chinese Academy of SciencesInstitute of AutomationBeijingChina
  3. 3.Chinese Academy of Sciences Institute of AutomatioBeijingChina
  4. 4.Tianjin University TianjinChina
  5. 5.IBM Research - ChinaBeijingChina

About the authors

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.

Bibliographic information

  • Book Title Adaptive Dynamic Programming with Applications in Optimal Control
  • Authors Derong Liu
    Qinglai Wei
    Ding Wang
    Xiong Yang
    Hongliang Li
  • Series Title Advances in Industrial Control
  • Series Abbreviated Title Advances in Industrial Control
  • DOI https://doi.org/10.1007/978-3-319-50815-3
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-50813-9
  • Softcover ISBN 978-3-319-84497-8
  • eBook ISBN 978-3-319-50815-3
  • Series ISSN 1430-9491
  • Series E-ISSN 2193-1577
  • Edition Number 1
  • Number of Pages XXX, 594
  • Number of Illustrations 28 b/w illustrations, 175 illustrations in colour
  • Topics Control and Systems Theory
    Computational Intelligence
    Applications of Nonlinear Dynamics and Chaos Theory
  • Buy this book on publisher's site
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