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Deep reinforcement learning with gua...

Zhang, Yinyan.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep reinforcement learning with guaranteed performance/ by Yinyan Zhang, Shuai Li, Xuefeng Zhou.
Reminder of title:	a Lyapunov-based approach /
Author:	Zhang, Yinyan.
other author:	Li, Shuai.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xvii, 225 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	A Survey of Near-Optimal Control of Nonlinear Systems -- Near-Optimal Control with Input Saturation -- Adaptive Near-Optimal Control with Full-State Feedback -- Adaptive Near-Optimal Control Using Sliding Mode -- Model-Free Adaptive Near-Optimal Tracking Control -- Adaptive Kinematic Control of Redundant Manipulators -- Redundancy Resolution with Periodic Input Disturbance.
Contained By:	Springer eBooks
Subject:	Control theory. -
Online resource:	https://doi.org/10.1007/978-3-030-33384-3
ISBN:	9783030333843

Deep reinforcement learning with guaranteed performance = a Lyapunov-based approach /
Zhang, Yinyan.

Deep reinforcement learning with guaranteed performancea Lyapunov-based approach /[electronic resource] :by Yinyan Zhang, Shuai Li, Xuefeng Zhou. - Cham :Springer International Publishing :2020. - xvii, 225 p. :ill. (some col.), digital ;24 cm. - Studies in systems, decision and control,v.2652198-4182 ;. - Studies in systems, decision and control ;v.265..

A Survey of Near-Optimal Control of Nonlinear Systems -- Near-Optimal Control with Input Saturation -- Adaptive Near-Optimal Control with Full-State Feedback -- Adaptive Near-Optimal Control Using Sliding Mode -- Model-Free Adaptive Near-Optimal Tracking Control -- Adaptive Kinematic Control of Redundant Manipulators -- Redundancy Resolution with Periodic Input Disturbance.

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.

ISBN: 9783030333843

Standard No.: 10.1007/978-3-030-33384-3doiSubjects--Topical Terms:

535880
Control theory.

LC Class. No.: QA402.3 / .Z436 2020

Dewey Class. No.: 629.8312

Deep reinforcement learning with guaranteed performance = a Lyapunov-based approach /
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245 1 0 $a Deep reinforcement learning with guaranteed performance $h [electronic resource] : $b a Lyapunov-based approach / $c by Yinyan Zhang, Shuai Li, Xuefeng Zhou.
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300 $a xvii, 225 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.265
505 0 $a A Survey of Near-Optimal Control of Nonlinear Systems -- Near-Optimal Control with Input Saturation -- Adaptive Near-Optimal Control with Full-State Feedback -- Adaptive Near-Optimal Control Using Sliding Mode -- Model-Free Adaptive Near-Optimal Tracking Control -- Adaptive Kinematic Control of Redundant Manipulators -- Redundancy Resolution with Periodic Input Disturbance.
520 $a 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.
650 0 $a Control theory. $3 535880
650 0 $a Lyapunov stability. $3 705168
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650 2 4 $a Robotics and Automation. $3 1066695
700 1 $a Li, Shuai. $3 3170629
700 1 $a Zhou, Xuefeng. $3 3445454
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950 $a Intelligent Technologies and Robotics (Springer-42732)