Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm
	Wei, Qinglai1 ; Song, Ruizhuo 2; Sun, Qiuye 3; Qinglai Wei
发表期刊	NEUROCOMPUTING
	2015-11-30
卷号	168 期号:x 页码:520-528
文章类型	Article
摘要	This paper discusses a new policy iteration Q-learning algorithm to solve the infinite horizon optimal tracking problems for a class of discrete-time nonlinear systems. The idea is to use an iterative adaptive dynamic programming (ADP) technique to construct the iterative tracking control law which makes the system state track the desired state trajectory and simultaneously minimizes the iterative Q function. Via system transformation, the optimal tracking problem is transformed into an optimal regulation problem. The policy iteration Q-learning algorithm is then developed to obtain the optimal control law for the regulation system. Initialized by an arbitrary admissible control law, the convergence property is analyzed. It is shown that the iterative Q function is monotonically non-increasing and converges to the optimal Q function. It is proven that any of the iterative control laws can stabilize the transformed nonlinear system. Two neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of policy iteration Q-learning algorithm. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm. (C) 2015 Elsevier B.V. All rights reserved.
关键词	Adaptive Dynamic Programming Approximate Dynamic Programming Q-learning Optimal Tracking Control Neural Networks
WOS标题词	Science & Technology ; Technology
关键词[WOS]	DYNAMIC-PROGRAMMING ALGORITHM ; CONTROL SCHEME ; FEEDBACK-CONTROL ; TIME-SYSTEMS ; REINFORCEMENT ; APPROXIMATION ; GAMES ; DELAY
URL	查看原文
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000359165000050
引用统计	被引频次：24[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/173211/8896
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Qinglai Wei
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 Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 3.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wei, Qinglai,Song, Ruizhuo,Sun, Qiuye,et al. Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm[J]. NEUROCOMPUTING,2015,168(x):520-528.
APA	Wei, Qinglai,Song, Ruizhuo,Sun, Qiuye,&Qinglai Wei.(2015).Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm.NEUROCOMPUTING,168(x),520-528.
MLA	Wei, Qinglai,et al."Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm".NEUROCOMPUTING 168.x(2015):520-528.

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