Reinforcement learning for robust adaptive control of partially unknown nonlinear systems subject to unmatched uncertainties
文献类型：期刊论文
| 作者 | Yang, Xiong1,2 ; He, Haibo2; Wei, Qinglai3; Luo, Biao3
|
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2018-10-01 |
| 卷号 | 463页码:307-322 |
| 关键词 | Adaptive Dynamic Programming Neural Networks Optimal Control Reinforcement Learning Robust Control Unmatched Uncertainty |
| DOI | 10.1016/j.ins.2018.06.022 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | CONTINUOUS-TIME SYSTEMS ; FAULT-TOLERANT CONTROL ; LAPLACIAN FRAMEWORK ; TRACKING CONTROL ; APPROXIMATION ; STABILIZATION ; FEEDBACK ; DESIGN ; ONLINE |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000442712900020 |
| 资助机构 | National Natural Science Foundation of China(61503379 ; China Scholarship Council ; National Science Foundation(CMMI 1526835) ; 61722312) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21865]  |
| 专题 | 自动化研究所_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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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