CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 智能化团队
Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties
Yang, Xiong1,2; He, Haibo2; Wei, Qinglai3; Luo, Biao3
Source PublicationINFORMATION SCIENCES
2018-10-01
Volume463Pages:307-322
SubtypeArticle
AbstractThis paper proposes a novel robust adaptive control strategy for partially unknown continuous-time nonlinear systems subject to unmatched uncertainties. Initially, the robust nonlinear control problem is converted into a nonlinear optimal control problem by constructing an appropriate value function for the auxiliary system. After that, within the framework of reinforcement learning, an identifier-critic architecture is developed. The presented architecture uses two neural networks: the identifier neural network (INN) which aims at estimating the unknown internal dynamics and the critic neural network (CNN) which tends to derive the approximate solution of the Hamilton-jacobi-Bellman equation arising in the obtained optimal control problem. The INN is updated by using both the back-propagation algorithm and the e-modification technique. Meanwhile, the CNN is updated via the modified gradient descent method, which uses historical and current state data simultaneously. Based on the classic Lyapunov technique, all the signals in the closed-loop auxiliary system are proved to be uniformly ultimately bounded. Moreover, the original system is kept asymptotically stable under the obtained approximate optimal control. Finally, two illustrative examples, including the F-16 aircraft plant, are provided to demonstrate the effectiveness of the developed method. (C) 2018 Elsevier Inc. All rights reserved.
KeywordAdaptive Dynamic Programming Neural Networks Optimal Control Reinforcement Learning Robust Control Unmatched Uncertainty
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.ins.2018.06.022
WOS KeywordCONTINUOUS-TIME SYSTEMS ; FAULT-TOLERANT CONTROL ; LAPLACIAN FRAMEWORK ; TRACKING CONTROL ; APPROXIMATION ; STABILIZATION ; FEEDBACK ; DESIGN ; ONLINE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61503379 ; China Scholarship Council ; National Science Foundation(CMMI 1526835) ; 61722312)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000442712900020
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21865
Collection复杂系统管理与控制国家重点实验室_智能化团队
Affiliation1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
2.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang, Xiong,He, Haibo,Wei, Qinglai,et al. Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties[J]. INFORMATION SCIENCES,2018,463:307-322.
APA Yang, Xiong,He, Haibo,Wei, Qinglai,&Luo, Biao.(2018).Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties.INFORMATION SCIENCES,463,307-322.
MLA Yang, Xiong,et al."Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties".INFORMATION SCIENCES 463(2018):307-322.
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