Self-Learning Optimal Control of Nonlinear Systems - Adaptive Dynamic Programming Approach (Hardcover, 1st ed. 2018)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Studies in Systems, Decision and Control, 103
Release date:	June 2017
First published:	2018
Authors:	Qinglai Wei • Ruizhuo Song • Benkai Li • Xiaofeng Lin
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	230
Edition:	1st ed. 2018
ISBN-13:	978-981-10-4079-5
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Professional & Technical > Mechanical engineering & materials > Materials science > Mechanics of solids > Dynamics & vibration Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General Promotions
LSN:	981-10-4079-6
Barcode:	9789811040795

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