@inproceedings{0f759d75fb1b409e94880e71a48f9541,
title = "Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning∗",
abstract = "In this paper, the formation problem of unknown multi-quadrotor systems with underactuation and nonlinearities is addressed. A formation controller including a position controller and an attitude controller is designed. The designed formation controller is based on hierarchical scheme and reinforcement learning method is used to learn the control weights of the formation controller. A simulation of formation of multiple quadrotor systems shows the effectiveness of the proposed controller.",
author = "Hao Liu and Wanbing Zhao and Lewis, {Frank L.} and Jiang, {Zhong Ping} and Hamidreza Modares",
note = "Funding Information: *This work was supported by the National Natural Science Foundation of China under Grants 61873012, 61503012, and 61633007, and the Office of Naval Research under Grant N00014-17-1-2239. Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9188809",
language = "English (US)",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6816--6821",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
}