Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties
Yang, Xiong1,2; He, Haibo2; Wei, Qinglai3; Luo, Biao3
2018-10-01
发表期刊INFORMATION SCIENCES
卷号463页码:307-322
文章类型Article
摘要This 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.
关键词Adaptive Dynamic Programming Neural Networks Optimal Control Reinforcement Learning Robust Control Unmatched Uncertainty
WOS标题词Science & Technology ; Technology
DOI10.1016/j.ins.2018.06.022
关键词[WOS]CONTINUOUS-TIME SYSTEMS ; FAULT-TOLERANT CONTROL ; LAPLACIAN FRAMEWORK ; TRACKING CONTROL ; APPROXIMATION ; STABILIZATION ; FEEDBACK ; DESIGN ; ONLINE
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61503379 ; China Scholarship Council ; National Science Foundation(CMMI 1526835) ; 61722312)
WOS研究方向Computer Science
WOS类目Computer Science, Information Systems
WOS记录号WOS:000442712900020
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21865
专题复杂系统管理与控制国家重点实验室_智能化团队
作者单位1.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
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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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