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On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear H-infinity Control
Wang, Ding1,2; Mu, Chaoxu3; Liu, Derong4; Ma, Hongwen1,2
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2018-04-01
Volume29Issue:4Pages:993-1005
SubtypeArticle
AbstractIn this paper, based on the adaptive critic learning technique, the H-infinity control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear H-infinity control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear H-infinity control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.
KeywordAdaptive Critic Designs Adaptive Dynamic Programming (Adp) Data Driven Control Event Driven Control Hamilton-jacobi-isaacs (Hji) Equation Neural Network Identification Nonlinear H-infinity Control Zero-sum Game
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2642128
WOS KeywordZERO-SUM GAMES ; DISCRETE-TIME-SYSTEMS ; STRICT-FEEDBACK FORM ; LEARNING ALGORITHM ; UNKNOWN DYNAMICS ; STATE-FEEDBACK ; STABILIZATION ; ITERATION ; SCHEME
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000427859600019
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21976
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Tianjin Univ, Sch Elect & Informat Engn, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China
4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
Recommended Citation
GB/T 7714
Wang, Ding,Mu, Chaoxu,Liu, Derong,et al. On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear H-infinity Control[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(4):993-1005.
APA Wang, Ding,Mu, Chaoxu,Liu, Derong,&Ma, Hongwen.(2018).On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear H-infinity Control.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(4),993-1005.
MLA Wang, Ding,et al."On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear H-infinity Control".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.4(2018):993-1005.
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