Knowledge Commons of Institute of Automation,CAS
Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning | |
Yang, Xiong1; Liu, Derong2; Luo, Biao3; Li, Chao3 | |
发表期刊 | INFORMATION SCIENCES |
2016-11-10 | |
卷号 | 369页码:731-747 |
文章类型 | Article |
摘要 | This paper presents a data-based robust adaptive control methodology for a class of nonlinear constrained-input systems with completely unknown dynamics. By introducing a value function for the nominal system, the robust control problem is transformed into a constrained optimal control problem. Due to the unavailability of system dynamics, a data-based integral reinforcement learning (RL) algorithm is developed to solve the constrained optimal control problem. Based on the present algorithm, the value function and the control policy*can be updated simultaneously using only system data. The convergence of the developed algorithm is proved via an established equivalence relationship. To implement the integral RL algorithm, an actor neural network (NN) and a critic NN are separately utilized to approximate the control policy and the value function, and the least squares method is employed to estimate the unknown parameters. By using Lyapunov's direct method, the obtained approximate optimal control is verified to guarantee the unknown nonlinear system to be stable in the sense of uniform ultimate boundedness. Two examples are provided to demonstrate the effectiveness and applicability of the theoretical results. (C) 2016 Elsevier Inc. All rights reserved. |
关键词 | Adaptive Dynamic Programming Input Constraint Neural Networks Optimal Control Reinforcement Learning Robust Control |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1016/j.ins.2016.07.051 |
关键词[WOS] | DYNAMIC-PROGRAMMING ALGORITHM ; CONTINUOUS-TIME SYSTEMS ; APPROXIMATE OPTIMAL-CONTROL ; OPTIMAL-CONTROL DESIGN ; ZERO-SUM GAME ; EXPERIENCE REPLAY ; TRACKING CONTROL ; NEURAL-NETWORKS ; CONTROL SCHEME ; FEEDBACK |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61233001 ; Early Career Development Award of the State Key Laboratory of Management and Control for Complex Systems (SKLMCCS) ; 61273140 ; 61304086 ; 61374105 ; 61503377 ; 61503379 ; 61533017 ; 131501251) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems |
WOS记录号 | WOS:000383292500046 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12632 |
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 Engn & Automat, Tianjin 300072, Peoples R China 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 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,Liu, Derong,Luo, Biao,et al. Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning[J]. INFORMATION SCIENCES,2016,369:731-747. |
APA | Yang, Xiong,Liu, Derong,Luo, Biao,&Li, Chao.(2016).Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning.INFORMATION SCIENCES,369,731-747. |
MLA | Yang, Xiong,et al."Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning".INFORMATION SCIENCES 369(2016):731-747. |
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