Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties

doi:10.1016/j.ins.2018.06.022

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

	Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties
	Yang, Xiong1,2 ; He, Haibo 2; Wei, Qinglai3 ; Luo, Biao3
发表期刊	INFORMATION SCIENCES
	2018-10-01
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
DOI	10.1016/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	被引频次：19[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/173211/21865
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
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
推荐引用方式 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.