@inproceedings{43f24bab5d0b4d06aaf556446e2ad2fd,
title = "Zero-Sum Game-Based Controller Design Using Reinforcement Learning for Formation Tracking of Multi-agent Systems",
abstract = "This paper proposes a hierarchical distributed formation tracking controller for continuous multi-agent systems (MASs). Firstly, a zero-sum game-based regulation controller is designed according to a min-max game cost function with respect to the control-player and a disturbance-player using reinforcement learning (RL). The H∞ robust property against perturbations is equivalently guaranteed. Secondly, the Q-learning based iteration method is implemented to continuous system to generate a stable controller. This regulator can be obtained using operational data associated with the original system. Thirdly, the distributed formation tracking controller with feedforward terms and feasible condition is further proposed based on the previous learning results to realize time-varying formation tracking while a brief stability analysis is given. Finally, simulation results are provided to illustrate the effectiveness of the proposed learning and control frame.",
keywords = "Distributed formation tracking, Multi-agent systems, Reinforcement learning, Robust control, Zero-sum game",
author = "Yu Shi and Xiwang Dong and Shaoqing Zhang and Jianglong Yu and Zhang Ren",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021 ; Conference date: 19-01-2022 Through 22-01-2022",
year = "2023",
doi = "10.1007/978-981-19-3998-3\_139",
language = "英语",
isbn = "9789811939976",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1487--1497",
editor = "Zhang Ren and Yongzhao Hua and Mengyi Wang",
booktitle = "Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control",
address = "德国",
}