Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

doi:10.1088/1674-1056/24/3/030502

›› 2015, Vol. 24 ›› Issue (3): 30502-030502.doi: 10.1088/1674-1056/24/3/030502

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

魏庆来, 刘德荣, 徐延才

The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

收稿日期:2014-07-22 修回日期:2014-10-09 出版日期:2015-03-05 发布日期:2015-03-05
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61034002, 61233001, 61273140, 61304086, and 61374105) and the Beijing Natural Science Foundation, China (Grant No. 4132078).

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

Wei Qing-Lai (魏庆来), Liu De-Rong (刘德荣), Xu Yan-Cai (徐延才)

The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received:2014-07-22 Revised:2014-10-09 Online:2015-03-05 Published:2015-03-05
Contact: Wei Qing-Lai E-mail:qinglai.wei@ia.ac.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61034002, 61233001, 61273140, 61304086, and 61374105) and the Beijing Natural Science Foundation, China (Grant No. 4132078).

摘要/Abstract

摘要： A policy iteration algorithm of adaptive dynamic programming (ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then, the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks, the developed optimal tracking control scheme for chaotic systems is verified by a simulation.

关键词: adaptive critic designs, adaptive dynamic programming, approximate dynamic programming, neuro-dynamic programming

Abstract: A policy iteration algorithm of adaptive dynamic programming (ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then, the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks, the developed optimal tracking control scheme for chaotic systems is verified by a simulation.

Key words: adaptive critic designs, adaptive dynamic programming, approximate dynamic programming, neuro-dynamic programming

中图分类号: (Control of chaos, applications of chaos)

05.45.Gg

魏庆来, 刘德荣, 徐延才. Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach[J]. , 2015, 24(3): 30502-030502.

Wei Qing-Lai (魏庆来), Liu De-Rong (刘德荣), Xu Yan-Cai (徐延才). Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach[J]. Chin. Phys. B, 2015, 24(3): 30502-030502.

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Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

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