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Deep reinforcement learning with gua...

Li, Shuai.

Deep reinforcement learning with guaranteed performancea Lyapunov-based approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep reinforcement learning with guaranteed performanceby Yinyan Zhang, Shuai Li, Xuefeng Zhou.
其他題名:	a Lyapunov-based approach /
作者:	Zhang, Yinyan.
其他作者:	Li, Shuai.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xvii, 225 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Control theory.
電子資源:	https://doi.org/10.1007/978-3-030-33384-3
ISBN:	9783030333843$q(electronic bk.)

Deep reinforcement learning with guaranteed performancea 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.3..

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$q(electronic bk.)

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

182248
Control theory.

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

Dewey Class. No.: 629.8312

Deep reinforcement learning with guaranteed performancea 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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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.
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