Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design
Wang, Ding1,2,3; He, Haibo3; Liu, Derong4
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
2017-10-01
Volume47Issue:10Pages:3417-3428
SubtypeArticle
Abstract

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
DOI10.1109/TCYB.2017.2653800
WOS KeywordContinuous-time Systems ; State-feedback Control ; Tracking Control ; Algorithm ; Iteration
Indexed BySCI
Language英语
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
Identifierhttp://ir.ia.ac.cn/handle/173211/20717
Collection复杂系统管理与控制国家重点实验室_复杂系统智能机理与平行控制团队
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
[J-2017-TCYB] Improv(1068KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Ding]'s Articles
[He, Haibo]'s Articles
[Liu, Derong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Ding]'s Articles
[He, Haibo]'s Articles
[Liu, Derong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Ding]'s Articles
[He, Haibo]'s Articles
[Liu, Derong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: [J-2017-TCYB] Improving the critic learning for event-based nonlinear H-infinity control design.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.