Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation
Wang, Ding1,2; Mu, Chaoxu2; Zhang, Qichao1; Liu, Derong3
2016-11-19
发表期刊NEUROCOMPUTING
卷号214期号:*页码:848-856
文章类型Article
摘要In this paper, the continuous-time input-constrained nonlinear H-infinity state feedback control under event based environment is investigated with adaptive critic designs and neural network implementation. The nonlinear H-infinity control issue is regarded as a two-player zero-sum game that requires solving the Hamilton-Jacobi-Isaacs equation and the adaptive critic learning (ACL) method is adopted toward the event-based constrained optimal regulation. The novelty lies in that the event-based design framework is combined with the ACL technique, thereby carrying out the input-constrained nonlinear H-infinity state feedback via adopting a non-quadratic utility function. The event-based optimal control law and the time-based worst-case disturbance law are derived approximately, by training an artificial neural network called a critic and eventually learning the optimal weight vector. Under the action of the event based state feedback controller, the closed-loop system is constructed with uniformly ultimately bounded stability analysis. Simulation studies are included to verify the theoretical results as well as to illustrate the event-based H-infinity control performance. (C) 2016 Elsevier B.V. All rights reserved.
关键词Adaptive Critic Learning (Acl) Adaptive Dynamic Programming (Adp) Event-based Control Hamilton-jacobi-isaacs (Hji) Equation Input Constraints Neural Networks Nonlinear H-infinity Control State Feedback
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2016.07.002
关键词[WOS]DISCRETE-TIME-SYSTEMS ; ZERO-SUM GAME ; TRACKING CONTROL ; NETWORKS ; ALGORITHM ; DYNAMICS ; EQUATION
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61233001 ; Beijing Natural Science Foundation(4162065) ; Tianjin Natural Science Foundation(14JCQNJC05400) ; Research Fund of Tianjin Key Laboratory of Process Measurement and Control(TKLPMC-201612) ; SKLMCCS ; 61273136 ; 61273140 ; 61304018 ; 61304086 ; 61533017 ; 61573353 ; 131501251 ; 61411130160)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000386741300080
引用统计
被引频次:15[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/173211/13360
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.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Tianjin Univ, Tianjin Key Lab Proc Measurement & Control, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China
3.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
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Wang, Ding,Mu, Chaoxu,Zhang, Qichao,et al. Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation[J]. NEUROCOMPUTING,2016,214(*):848-856.
APA Wang, Ding,Mu, Chaoxu,Zhang, Qichao,&Liu, Derong.(2016).Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation.NEUROCOMPUTING,214(*),848-856.
MLA Wang, Ding,et al."Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation".NEUROCOMPUTING 214.*(2016):848-856.
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