Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems
Wei Qing-Lai1; Song Rui-Zhuo2; Sun Qiu-Ye3; Xiao Wen-Dong2
2015-09-01
发表期刊CHINESE PHYSICS B
卷号24期号:9
文章类型Article
摘要This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton-Jacobi-Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.
关键词Adaptive Dynamic Programming Approximate Dynamic Programming Chaotic System Optimal Tracking Control
WOS标题词Science & Technology ; Physical Sciences
DOI10.1088/1674-1056/24/9/090504
关键词[WOS]ATTRACTOR ; DYNAMICS ; DESIGN
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61304079 ; Beijing Natural Science Foundation, China(4132078 ; China Postdoctoral Science Foundation(2013M530527) ; Fundamental Research Funds for the Central Universities, China(FRF-TP-14-119A2) ; Open Research Project from State Key Laboratory of Management and Control for Complex Systems, China(20150104) ; 61374105) ; 4143065)
WOS研究方向Physics
WOS类目Physics, Multidisciplinary
WOS记录号WOS:000363325200021
引用统计
被引频次:5[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10493
专题复杂系统管理与控制国家重点实验室_平行控制
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Sci & Technol, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
3.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China
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Wei Qing-Lai,Song Rui-Zhuo,Sun Qiu-Ye,et al. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems[J]. CHINESE PHYSICS B,2015,24(9).
APA Wei Qing-Lai,Song Rui-Zhuo,Sun Qiu-Ye,&Xiao Wen-Dong.(2015).Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems.CHINESE PHYSICS B,24(9).
MLA Wei Qing-Lai,et al."Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems".CHINESE PHYSICS B 24.9(2015).
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