中文版 | English
Title

Output feedback Q-learning for discrete-time finite-horizon zero-sum games with application to the H-? control

Author
Corresponding AuthorCai, Qianqian
Publication Years
2023-04-07
DOI
Source Title
ISSN
0925-2312
EISSN
1872-8286
Volume529Pages:48-55
Abstract
In this paper, we present a Q-learning framework for solving finite-horizon zero-sum game problems involving the H. control of linear system without knowing the dynamics. Research in the past mainly focused on solving problems in infinite horizon with completely measurable state. However, in the prac-tical engineering, the system state is not always directly accessible, and it is difficult to solve the time-varying Riccati equation associated with the finite-horizon setting directly either. The main contribution of the proposed model-free algorithm is to determine the optimal output feedback policies without mea-surement state in finite-horizon setting. To achieve this goal, we first describe the Q-function caused by finite-horizon problems in the context of state feedback, then we parameterize the Q-functions as input- output vectors functions. Finally, the numerical examples on aircraft dynamics demonstrate the algo-rithm's efficiency. (c) 2023 Published by Elsevier B.V.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
Grants of National Natural Science Foundation of China["U21A20476","U1911401","U22A20221","62273100","62073090"] ; Guang-dong Basic and Applied Basic Research Foundation["2021A1515012554","2020A1515011505"]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence
WOS Accession No
WOS:000935337000001
Publisher
ESI Research Field
COMPUTER SCIENCE
Scopus EID
2-s2.0-85149757845
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/501408
DepartmentDepartment of Mechanical and Energy Engineering
Affiliation
1.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
2.Guangdong Univ Technol, Guangdong Prov Key Lab Intelligent Decis & Coopera, Guangzhou 510006, Guangdong, Peoples R China
3.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Liu, Mingxiang,Cai, Qianqian,Li, Dandan,et al. Output feedback Q-learning for discrete-time finite-horizon zero-sum games with application to the H-? control[J]. NEUROCOMPUTING,2023,529:48-55.
APA
Liu, Mingxiang,Cai, Qianqian,Li, Dandan,Meng, Wei,&Fu, Minyue.(2023).Output feedback Q-learning for discrete-time finite-horizon zero-sum games with application to the H-? control.NEUROCOMPUTING,529,48-55.
MLA
Liu, Mingxiang,et al."Output feedback Q-learning for discrete-time finite-horizon zero-sum games with application to the H-? control".NEUROCOMPUTING 529(2023):48-55.
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