Last edited by Misho
Tuesday, July 21, 2020 | History

1 edition of Adaptive Dynamic Programming for Control found in the catalog.

Adaptive Dynamic Programming for Control

Algorithms and Stability

by Huaguang Zhang

  • 211 Want to read
  • 37 Currently reading

Published by Springer London, Imprint: Springer in London .
Written in English

    Subjects:
  • Control,
  • Artificial intelligence,
  • System theory,
  • Engineering,
  • Optimization,
  • Artificial Intelligence (incl. Robotics),
  • Mathematical optimization,
  • Computational intelligence,
  • Control Systems Theory

  • Edition Notes

    Statementby Huaguang Zhang, Derong Liu, Yanhong Luo, Ding Wang
    SeriesCommunications and Control Engineering
    ContributionsLiu, Derong, Luo, Yanhong, Wang, Ding, SpringerLink (Online service)
    Classifications
    LC ClassificationsTJ212-225
    The Physical Object
    Format[electronic resource] :
    PaginationXV, 424 p. 163 illus., 5 illus. in color.
    Number of Pages424
    ID Numbers
    Open LibraryOL27014402M
    ISBN 109781447147572

    By adopting an adaptive dynamic programming technique with sampled-data system theory, a data-driven adaptive optimal control approach is proposed for autonomous vehicles by the learning.   Adaptive Dynamic Programming for Control by Huaguang Zhang, , available at Book Depository with free delivery worldwide.4/5(1).

    In this paper, the optimal output tracking control problem of discrete-time nonlinear systems is considered. First, the augmented system is derived and the Output Tracking Control Based on Adaptive Dynamic Programming With Multistep Policy Evaluation - IEEE Journals & MagazineCited by: 8. Adaptive Dynamic Programming for Control by Huaguang Zhang, , available at Book Depository with free delivery worldwide.4/5(1).

    This book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven. Yang X, Liu D, Wei Q () Online approximate optimal control for affine non-linear systems with unknown internal dynamics using adaptive dynamic programming. IET Control Theory Appl 8(16)– MathSciNet CrossRef Google ScholarCited by: 1.


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Adaptive Dynamic Programming for Control by Huaguang Zhang Download PDF EPUB FB2

In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural Format: Hardcover.

This book presents a class of novel optimal control methods and games schemes based on adaptive dynamic programming techniques. For systems with one control input, the ADP-based optimal control is designed for different objectives, while for systems with multi-players, the optimal control inputs are proposed based on cturer: Springer.

In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP).

The range of systems treated is. 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- Cited by: In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP).

The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural. There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP).

This book presents a class of novel optimal control methods and games schemes based on adaptive dynamic programming techniques. For systems with one control input, the ADP-based optimal control is designed for different objectives, while for systems with multi-players, the optimal control inputs are proposed based on games.

This book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven optimal control design challenges arising from both parametric and dynamic uncertainties.

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.

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. Motivated by issues arising in adaptive dynamic programming for optimal control, a function approximation method is developed that aims to approximate a function.

Index Terms—Adaptive dynamic programming, nonlinear sys-tems, optimal control, global stabilization. INTRODUCTION Dynamic programming [4] offers a theoretical way to solve optimal control problems. However, it suffers from the in-herent computational complexity, also known as the curse of dimensionality.

Therefore, the need for. Click here for an extended lecture/summary of the book: Ten Key Ideas for Reinforcement Learning and Optimal Control. The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable.

Robust Adaptive Dynamic Programming The authors develop robust adaptive dynamic programming (RADP) theory from linear systems to partially-linear, large-scale, and completely nonlinear systems. They provide in-depth coverage of state-of-the-art applications in power systems, supplemented with numerous real-world examples implemented in MATLAB and Simulink.

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. This book approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP).

It shows readers how to derive necessary stability and convergence criteria for their own systems. Adaptive dynamic programming (ADP), as an important optimal control technique, can be exploited in the setting of data-driven control based on an approximate regression-based solution of the.

A comprehensive look at state-of-the-art ADP theory and real-world applications. This book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven optimal control design challenges arising from both parametric and dynamic uncertainties.

Adaptive Control and Reinforcement Learning (Spring ) Deep Reinforcement Learning for Robotics; Deep Reinforcement Learning and Control; Deep Reinforcement Learning and Control (undergrad version) CMU ChemE: Advanced Process Systems Engineering ; CMU Tepper: Dynamic Programming, Nonlinear Programming.

His current research interests include robust output-feedback control, adaptive dynamic programming and adaptive parameter identification of industrial robots. Jing Na (M’5) received the and Ph.D. degrees from the School of Automation, Beijing Institute of Author: Jun Zhao, Jing Na, Guanbin Gao.

Adaptive Dynamic Programming for Control. by Huaguang Zhang,Derong Liu,Yanhong Luo,Ding Wang. Communications and Control Engineering. Share your thoughts Complete your review. Tell readers what you thought by rating and reviewing this book. Rate it * You Rated it *Brand: Springer London.

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems.

This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA) which fuses soft computing techniques to learn the optimal cost (or return) functional for a stabilizable nonlinear system with unknown dynamics and hard computing techniques to verify the stability and convergence of the by: