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Self-Learning Optimal Control of Nonlinear Systems

Adaptive Dynamic Programming Approach

Authors:

  • Provides a series of novel adaptive dynamic programming methodologies to obtain the optimal control policies for various nonlinear systems

  • Includes a detailed theoretical analysis of the adaptive dynamic programming methods, which guarantees the effectiveness of the methods developed

  • Presents substantial application examples to support the theoretical results

  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 103)

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  • ISBN: 978-981-10-4080-1
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Softcover Book USD 169.99
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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xviii
  2. Principle of Adaptive Dynamic Programming

    • Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin
    Pages 1-17
  3. Discrete-Time Optimal Control of Nonlinear Systems via Value Iteration-Based \( Q \) -Learning

    • Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin
    Pages 47-84
  4. A Novel Policy Iteration-Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems

    • Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin
    Pages 85-109
  5. Nonlinear Neuro-Optimal Tracking Control via Stable Iterative Q-Learning Algorithm

    • Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin
    Pages 111-131
  6. Off-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems

    • Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin
    Pages 201-214
  7. Erratum to: Self-Learning Optimal Control of Nonlinear Systems

    • Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin
    Pages E1-E1
  8. Back Matter

    Pages 229-230

About this book

This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum.

With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.

Keywords

  • Intelligence Control
  • Adaptive Dynamic Programming
  • Self-Learning Control
  • Nonlinear Systems
  • Neural Network-Based Control

Reviews

“Book contains various real-world examples to illustrate the developed mathematical analysis. Thus, it is a valuable and important guide for engineers, researchers, and students in systems, decision and control science.” (Savin Treanta, zbMATH 1403.49002, 2019)

Authors and Affiliations

  • Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Qinglai Wei, Benkai Li

  • University of Science and Technology Beijing, Beijing, China

    Ruizhuo Song

  • Guangxi University, Guangxi, China

    Xiaofeng Lin

About the authors

Qinglai Wei received his B.S. degree in Automation and Ph.D. degree in Control Theory and Control Engineering from the Northeastern University, Shenyang, China, in 2002 and 2009, respectively. 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 and is currently a professor there. He has authored one book and published over 70 international journal papers. His research interests include adaptive dynamic programming, neural-networks-based control, optimal control, nonlinear systems and their industrial applications.

Dr. 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 held the same position for IEEE Transactions on Neural Networks and Learning Systems from 2014 to 2015. He has been the secretary of the IEEE Computational Intelligence Society (CIS) Beijing Chapter since 2015. He was registration chair of the 12th World Congress on Intelligent Control and Automation (WCICA 2016), the IEEE World Congress on Computational Intelligence (WCCI 2014), the International Conference on Brain Inspired Cognitive Systems (BICS 2013), and the 8th International Symposium on Neural Networks (ISNN 2011). He was the publication chair of the 5th International Conference on Information Science and Technology (ICIST 2015) and the 9th International Symposium on Neural Networks (ISNN 2012). He was the finance chair of the 4th International Conference on Intelligent Control and Information Processing (ICICIP 2013) and the publicity chair of the International Conference on Brain Inspired Cognitive Systems (BICS 2012). He has been the guest editor for several international journals. He was a recipient of the Acta Automatica Sinica Outstanding Paper Award in 2011 and the Chinese Control, Decision Conference (CCDC) Zhang Siying Outstanding Paper Award in 2015, and Young Researcher Award of Asia Pacific Neural Network Society (APNNS) in 2016.

Ruizhuo Song received his Ph.D. degree in Control Theory and Control Engineering from Northeastern University, Shenyang, China, in 2012. She is currently an associate professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. Her research interests include optimal control, neural-network-based control, nonlinear control, wireless sensor networks, adaptive dynamic programming and their industrial application.

Bibliographic Information

  • Book Title: Self-Learning Optimal Control of Nonlinear Systems

  • Book Subtitle: Adaptive Dynamic Programming Approach

  • Authors: Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin

  • Series Title: Studies in Systems, Decision and Control

  • DOI: https://doi.org/10.1007/978-981-10-4080-1

  • Publisher: Springer Singapore

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Science Press, Beijing and Springer Nature Singapore Pte Ltd. 2018

  • Hardcover ISBN: 978-981-10-4079-5

  • Softcover ISBN: 978-981-13-5043-6

  • eBook ISBN: 978-981-10-4080-1

  • Series ISSN: 2198-4182

  • Series E-ISSN: 2198-4190

  • Edition Number: 1

  • Number of Pages: XVIII, 230

  • Number of Illustrations: 13 b/w illustrations, 73 illustrations in colour

  • Additional Information: Jointly published with Science Press, Beijing, China

  • Topics: Control and Systems Theory, Computational Intelligence, Multibody Systems and Mechanical Vibrations

Buying options

eBook USD 129.00
Price excludes VAT (USA)
  • ISBN: 978-981-10-4080-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 169.99
Price excludes VAT (USA)
Hardcover Book USD 169.99
Price excludes VAT (USA)