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Self-learning optimal control of non...

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Self-learning optimal control of nonlinear systemsadaptive dynamic programming approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Self-learning optimal control of nonlinear systemsby Qinglai Wei ... [et al.].
其他題名:	adaptive dynamic programming approach /
其他作者:	Wei, Qinglai.
出版者:	Singapore :Springer Singapore :2018.
面頁冊數:	xvi, 230 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Dynamic programming.
電子資源:	http://dx.doi.org/10.1007/978-981-10-4080-1
ISBN:	9789811040801$q(electronic bk.)

Self-learning optimal control of nonlinear systemsadaptive 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.3..

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$q(electronic bk.)

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

181912
Dynamic programming.

LC Class. No.: T57.83

Dewey Class. No.: 519.703

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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.
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