Self-Learning Optimal Control of Nonlinear Systems

Adaptive Dynamic Programming Approach

Nonfiction, Science & Nature, Technology, Automation, Computers, Advanced Computing, Artificial Intelligence
Cover of the book Self-Learning Optimal Control of Nonlinear Systems by Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin, Springer Singapore
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Author: Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin ISBN: 9789811040801
Publisher: Springer Singapore Publication: June 13, 2017
Imprint: Springer Language: English
Author: Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin
ISBN: 9789811040801
Publisher: Springer Singapore
Publication: June 13, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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