Self-Learning Optimal Control of Nonlinear Systems Adaptive Dynamic Programming Approach /

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 ite...

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Detaylı Bibliyografya
Asıl Yazarlar: Wei, Qinglai (Yazar), Song, Ruizhuo (Yazar), Li, Benkai (Yazar), Lin, Xiaofeng (Yazar)
Müşterek Yazar: SpringerLink (Online service)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Singapore : Springer Nature Singapore : 2018.
Imprint: Springer,
Edisyon:1st ed. 2018.
Seri Bilgileri:Studies in Systems, Decision and Control, 103
Konular:
Control engineering.
Computational intelligence.
Multibody systems.
Vibration.
Mechanics, Applied.
Control and Systems Theory.
Computational Intelligence.
Multibody Systems and Mechanical Vibrations.
Online Erişim:Full-text access
Diğer Bilgiler
Özet: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.
Fiziksel Özellikler:XVIII, 230 p. 86 illus., 73 illus. in color. online resource.
ISBN:9789811040801
ISSN:2198-4190 ;
DOI:10.1007/978-981-10-4080-1

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