Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning

doi:10.1016/j.ins.2016.07.051

CASIA OpenIR > 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

	Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning
	Yang, Xiong1 ; Liu, Derong 2; 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	被引频次：68[WOS] [WOS记录] [WOS相关记录]
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.