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

Liu, Mingxiang1,2; Cai, Qianqian1,2; Li, Dandan1,2; Meng, Wei1,2; Fu, Minyue3

2023-04-07

10.1016/j.neucom.2023.01.050

NEUROCOMPUTING   Impact Factor & Quartile

0925-2312

1872-8286

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.

Finite-horizon Output feedback Zero -sum games H control Q-learning Linear quadratic (LQ)

SCI

English

Others

Grants of National Natural Science Foundation of China["U21A20476","U1911401","U22A20221","62273100","62073090"] ; Guang-dong Basic and Applied Basic Research Foundation["2021A1515012554","2020A1515011505"]

Computer Science

Computer Science, Artificial Intelligence

WOS:000935337000001

ELSEVIER

COMPUTER SCIENCE

2-s2.0-85149757845

Web of Science

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

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.

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.

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.