Deep reinforcement learning with guaranteed performance : a Lyapunov-based approach /

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Bibliographic Details
Author / Creator:Zhang, Yinyan, author.
Imprint:Cham, Switzerland : Springer, [2020].
Description:1 online resource (xvii, 225 pages) : illustrations (some color).
Language:English
Series:Studies in systems, decision and control, 2198-4182 ; volume 265
Studies in systems, decision and control ; v. 265.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12602492
Hidden Bibliographic Details
Other authors / contributors:Li, Shuai, 1983- author.
Zhou, Xuefeng, author.
ISBN:9783030333843
3030333841
9783030333836
3030333833
Notes:Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed November 13, 2019).
Summary:This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
Other form:Original 3030333833 9783030333836
Standard no.:10.1007/978-3-030-33384-3
10.1007/978-3-030-33