Knowledge Commons of Institute of Automation,CAS
Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties | |
Yang, Xiong1,2; He, Haibo2; 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 | |
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. |
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