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Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems

  • Book
  • © 2019

Overview

Authors:
  1. Ding Wang

    1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

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  2. Chaoxu Mu

    1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

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  • Provides a comprehensive and critical review of the latest advances in adaptive critic based robust control design for nonlinear systems
  • Discusses data-based robust stabilization, trajectory tracking, and event-driven disturbance attenuation
  • Includes theoretical analysis, algorithm design, simulation verification, and experimental results
  • Focuses on robust adaptive critic control methods, and the construction of higher-level intelligent systems

Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 167)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Overview of Robust Adaptive Critic Control Design

    • Ding Wang, Chaoxu Mu
    Pages 1-43

  3. Robust Optimal Control of Nonlinear Systems with Matched Uncertainties

    • Ding Wang, Chaoxu Mu
    Pages 45-84

  4. Observer-Based Online Adaptive Regulation for a Class of Uncertain Nonlinear Systems

    • Ding Wang, Chaoxu Mu
    Pages 85-115

  5. Adaptive Tracking Control of Nonlinear Systems Subject to Matched Uncertainties

    • Ding Wang, Chaoxu Mu
    Pages 117-144

  6. Event-Triggered Robust Stabilization Incorporating an Adaptive Critic Mechanism

    • Ding Wang, Chaoxu Mu
    Pages 145-172

  7. An Improved Adaptive Optimal Regulation Framework with Robust Control Synthesis

    • Ding Wang, Chaoxu Mu
    Pages 173-197

  8. Robust Stabilization and Trajectory Tracking of General Uncertain Nonlinear Systems

    • Ding Wang, Chaoxu Mu
    Pages 199-227

  9. Event-Triggered Nonlinear \(H_{\infty }\) Control Design via an Improved Critic Learning Strategy

    • Ding Wang, Chaoxu Mu
    Pages 229-255

  10. Intelligent Critic Control with Disturbance Attenuation for a Micro-Grid System

    • Ding Wang, Chaoxu Mu
    Pages 257-280

  11. ADP-Based Supplementary Design for Load Frequency Control of Power Systems

    • Ding Wang, Chaoxu Mu
    Pages 281-304

  12. Back Matter

    Pages 305-307
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Keywords

About this book

This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical engineering wishing to learn about the fundamental principles, methods, algorithms, and applications in the field of robust adaptive critic control. In addition, it promotes the development of robust adaptive critic control approaches, and the construction of higher-level intelligent systems.
 

Reviews

“The book presents results on learning-based robust adaptive critic control theory, including self-learning robust stabilization, data-driven robust optimal control, adaptive trajectory tracking, adaptive H1 control design. A general analysis for adaptive critic systems in terms of stability, convergence, optimality, and robustness under uncertain environment is covered.” (Alexandra Rodkina, zbMATH 1407.93006, 2019)

Authors and Affiliations

  • The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ding Wang

  • School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Chaoxu Mu

About the authors

Dr. Ding Wang is an associate professor in The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His main research interests cover adaptive and learning control systems, complex systems and intelligent control, neural networks and neural computing.

Dr. Chaoxu Mu is an associate professor in school of electrical and information engineering, Tianjin University. Her research interests focus mainly on non-linear control theory and applications, adaptive dynamic programming and robust control.

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