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
2016-11-10
发表期刊INFORMATION SCIENCES
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
DOI10.1016/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
专题复杂系统管理与控制国家重点实验室_平行控制
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
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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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