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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
Source PublicationINFORMATION SCIENCES
2016-11-10
Volume369Pages:731-747
SubtypeArticle
AbstractThis 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.
KeywordAdaptive Dynamic Programming Input Constraint Neural Networks Optimal Control Reinforcement Learning Robust Control
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.ins.2016.07.051
WOS KeywordDYNAMIC-PROGRAMMING ALGORITHM ; CONTINUOUS-TIME SYSTEMS ; APPROXIMATE OPTIMAL-CONTROL ; OPTIMAL-CONTROL DESIGN ; ZERO-SUM GAME ; EXPERIENCE REPLAY ; TRACKING CONTROL ; NEURAL-NETWORKS ; CONTROL SCHEME ; FEEDBACK
Indexed BySCI
Language英语
Funding OrganizationNational 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 Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000383292500046
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12632
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.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
Recommended Citation
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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