Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP

doi:10.1109/TNNLS.2015.2464080

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-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP
	Wei, Qinglai1 ; Song, Ruizhuo 2; Yan, Pengfei1
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2016-02-01
卷号	27 期号:2 页码:444-458
文章类型	Article
摘要	This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming ( ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.
关键词	Adaptive Critic Designs Adaptive Dynamic Programming (Adp) Approximate Dynamic Programming Neurodynamic Programming Nonlinear Systems Optimal Control Recurrent Neural Network (Rnn) Reinforcement Learning
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TNNLS.2015.2464080
关键词[WOS]	OPTIMAL TRACKING CONTROL ; DYNAMIC-PROGRAMMING ALGORITHM ; ADAPTIVE OPTIMAL-CONTROL ; OPTIMAL-CONTROL SCHEME ; KERNEL HILBERT-SPACES ; H-INFINITY CONTROL ; FEEDBACK-CONTROL ; APPROXIMATION ERRORS ; STABILITY ANALYSIS ; POLICY ITERATION
收录类别	SCI
语种	英语
项目资助者	Beijing Natural Science Foundation(4132078 ; National Natural Science Foundation of China(61034002 ; China Postdoctoral Science Foundation(2013M530527) ; Fundamental Research Funds for the Central Universities(FRF-TP-14-119A2) ; Open Research Project from SKLMCCS(20150104) ; Early Career Development Award of State Key Laboratory of Management and Control for Complex Systems ; 4143065) ; 61304079 ; 61273140 ; 61374105)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000372020500021
引用统计	被引频次：191[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/173211/11368
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.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wei, Qinglai,Song, Ruizhuo,Yan, Pengfei. Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2016,27(2):444-458.
APA	Wei, Qinglai,Song, Ruizhuo,&Yan, Pengfei.(2016).Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,27(2),444-458.
MLA	Wei, Qinglai,et al."Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 27.2(2016):444-458.

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