Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design
Wang, Ding1,2,3; He, Haibo3; Liu, Derong4

In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H-infinity state feedback control design. First of all, the H-infinity control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.

KeywordH-infinity Control Adaptive Systems Adaptive/approximate Dynamic Programming Critic Network Event-based Design Learning Criterion Neural Control
WOS HeadingsScience & Technology ; Technology
WOS KeywordContinuous-time Systems ; State-feedback Control ; Tracking Control ; Algorithm ; Iteration
Indexed BySCI
Funding OrganizationBeijing Natural Science Foundation(4162065) ; National Natural Science Foundation of China(51529701 ; U.S. National Science Foundation(ECCS 1053717) ; SKLMCCS ; 61304086 ; U1501251 ; 61533017 ; 61233001 ; 61273140 ; 61520106009)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000409311800038
Citation statistics
Cited Times:31[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA
4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Ding,He, Haibo,Liu, Derong. Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(10):3417-3428.
APA Wang, Ding,He, Haibo,&Liu, Derong.(2017).Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design.IEEE TRANSACTIONS ON CYBERNETICS,47(10),3417-3428.
MLA Wang, Ding,et al."Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design".IEEE TRANSACTIONS ON CYBERNETICS 47.10(2017):3417-3428.
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