Wenjun Wei1, 2 and Jingyuan Tang This email address is being protected from spambots. You need JavaScript enabled to view it.1

1School of Automation & Electrical Engineering, Lanzhou Jiao tong University, An Ning Road, Lanzhou 730070, Gansu. China
2The key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiao tong University, An Ning Road, Lanzhou 730070, Gansu. China


 

Received: March 27, 2022
Accepted: June 28, 2022
Publication Date: September 20, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202306_26(6).0011  


ABSTRACT


This article studies the output regulation of discrete-time multi-agent systems with an unknown model by a finite-time optimal control algorithm based on Q-learning that uses the method of the linear quadratic regulator (LQR). The algorithm uses the Bellman optimality principle to deduce the Q-function under global optimality. It obtains the distributed optimal control law that minimizes the value of Q-function by policy iteration. Through local communication among agents, the optimal global control of each agent’s output can be realized without relying on the dynamic model of the system. Secondly, by designing a novel finite-time local error formula, the output regulation synchronization time is reduced by 50%. Finally, a MATLAB simulation example shows the capability of the nominated algorithm.


Keywords: Discrete multi-agent systems, Q-learning, cooperative output regulation, fast convergence


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