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Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm
Wei, Qinglai1; Song, Ruizhuo2; Sun, Qiuye3; Qinglai Wei
Source PublicationNEUROCOMPUTING
2015-11-30
Volume168Issue:xPages:520-528
SubtypeArticle
AbstractThis 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.
KeywordAdaptive Dynamic Programming Approximate Dynamic Programming Q-learning Optimal Tracking Control Neural Networks
WOS HeadingsScience & Technology ; Technology
WOS KeywordDYNAMIC-PROGRAMMING ALGORITHM ; CONTROL SCHEME ; FEEDBACK-CONTROL ; TIME-SYSTEMS ; REINFORCEMENT ; APPROXIMATION ; GAMES ; DELAY
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Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000359165000050
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8896
Collection复杂系统管理与控制国家重点实验室_平行控制
Corresponding AuthorQinglai Wei
Affiliation1.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
Recommended Citation
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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