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Self-learning optimal control of non...

Wei, Qinglai.

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Self-learning optimal control of nonlinear systems = adaptive dynamic programming approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Self-learning optimal control of nonlinear systems/ by Qinglai Wei ... [et al.].
Reminder of title:	adaptive dynamic programming approach /
other author:	Wei, Qinglai.
Published:	Singapore :Springer Singapore : : 2018.,
Description:	xvi, 230 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Principle of Adaptive Dynamic Programming -- Chapter 2. An Iterative ϵ-Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems With Unﬁxed Initial State -- Chapter 3. Discrete-Time Optimal Control of Nonlinear Systems Via Value Iteration-Based Q-Learning -- Chapter 4. A Novel Policy Iteration Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems -- Chapter 5. Nonlinear Neuro-Optimal Tracking Control Via Stable Iterative Q-Learning Algorithm -- Chapter 6. Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions -- Chapter 7. Multi-Objective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm -- Chapter 8. A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm -- Chapter 9. Oﬀ-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems -- Chapter 10. ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks.
Contained By:	Springer eBooks
Subject:	Dynamic programming. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-4080-1
ISBN:	9789811040801

Self-learning optimal control of nonlinear systems = adaptive dynamic programming approach /
Self-learning optimal control of nonlinear systemsadaptive dynamic programming approach /[electronic resource] :by Qinglai Wei ... [et al.]. - Singapore :Springer Singapore :2018. - xvi, 230 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.1032198-4182 ;. - Studies in systems, decision and control ;v.103..

Chapter 1. Principle of Adaptive Dynamic Programming -- Chapter 2. An Iterative ϵ-Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems With Unﬁxed Initial State -- Chapter 3. Discrete-Time Optimal Control of Nonlinear Systems Via Value Iteration-Based Q-Learning -- Chapter 4. A Novel Policy Iteration Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems -- Chapter 5. Nonlinear Neuro-Optimal Tracking Control Via Stable Iterative Q-Learning Algorithm -- Chapter 6. Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions -- Chapter 7. Multi-Objective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm -- Chapter 8. A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm -- Chapter 9. Oﬀ-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems -- Chapter 10. ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks.

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.

ISBN: 9789811040801

Standard No.: 10.1007/978-981-10-4080-1doiSubjects--Topical Terms:

641303
Dynamic programming.

LC Class. No.: T57.83

Dewey Class. No.: 519.703

Self-learning optimal control of nonlinear systems = adaptive dynamic programming approach /
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245 0 0 $a Self-learning optimal control of nonlinear systems $h [electronic resource] : $b adaptive dynamic programming approach / $c by Qinglai Wei ... [et al.].
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300 $a xvi, 230 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.103
505 0 $a Chapter 1. Principle of Adaptive Dynamic Programming -- Chapter 2. An Iterative ϵ-Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems With Unﬁxed Initial State -- Chapter 3. Discrete-Time Optimal Control of Nonlinear Systems Via Value Iteration-Based Q-Learning -- Chapter 4. A Novel Policy Iteration Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems -- Chapter 5. Nonlinear Neuro-Optimal Tracking Control Via Stable Iterative Q-Learning Algorithm -- Chapter 6. Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions -- Chapter 7. Multi-Objective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm -- Chapter 8. A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm -- Chapter 9. Oﬀ-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems -- Chapter 10. ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks.
520 $a 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.
650 0 $a Dynamic programming. $3 641303
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650 1 4 $a Engineering. $3 586835
650 2 4 $a Control. $3 1006321
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Vibration, Dynamical Systems, Control. $3 893843
700 1 $a Wei, Qinglai. $3 3295251
710 2 $a SpringerLink (Online service) $3 836513
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830 0 $a Studies in systems, decision and control ; $v v.103. $3 3295252
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