Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning∗

Hao Liu; Wanbing Zhao; Frank L. Lewis; Zhong Ping Jiang; Hamidreza Modares

doi:10.23919/CCC50068.2020.9188809

Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning^∗

Hao Liu, Wanbing Zhao, Frank L. Lewis, Zhong Ping Jiang, Hamidreza Modares

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Proceedings of the 39th Chinese Control Conference, CCC 2020
Editors	Jun Fu, Jian Sun
Publisher	IEEE Computer Society
Pages	6816-6821
Number of pages	6
ISBN (Electronic)	9789881563903
DOIs	https://doi.org/10.23919/CCC50068.2020.9188809
State	Published - Jul 2020
Event	39th Chinese Control Conference, CCC 2020 - Shenyang, China Duration: Jul 27 2020 → Jul 29 2020

Publication series

Name	Chinese Control Conference, CCC
Volume	2020-July
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	39th Chinese Control Conference, CCC 2020
Country/Territory	China
City	Shenyang
Period	7/27/20 → 7/29/20

ASJC Scopus subject areas

Computer Science Applications
Control and Systems Engineering
Applied Mathematics
Modeling and Simulation

Access to Document

10.23919/CCC50068.2020.9188809

Cite this

Liu, H., Zhao, W., Lewis, F. L., Jiang, Z. P., & Modares, H. (2020). Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning^∗. In J. Fu, & J. Sun (Eds.), Proceedings of the 39th Chinese Control Conference, CCC 2020 (pp. 6816-6821). Article 9188809 (Chinese Control Conference, CCC; Vol. 2020-July). IEEE Computer Society. https://doi.org/10.23919/CCC50068.2020.9188809

Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning^∗. / Liu, Hao; Zhao, Wanbing; Lewis, Frank L. et al.
Proceedings of the 39th Chinese Control Conference, CCC 2020. ed. / Jun Fu; Jian Sun. IEEE Computer Society, 2020. p. 6816-6821 9188809 (Chinese Control Conference, CCC; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, H, Zhao, W, Lewis, FL, Jiang, ZP & Modares, H 2020, Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning^∗. in J Fu & J Sun (eds), Proceedings of the 39th Chinese Control Conference, CCC 2020., 9188809, Chinese Control Conference, CCC, vol. 2020-July, IEEE Computer Society, pp. 6816-6821, 39th Chinese Control Conference, CCC 2020, Shenyang, China, 7/27/20. https://doi.org/10.23919/CCC50068.2020.9188809

@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",

}

TY - GEN

T1 - Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning∗

AU - Liu, Hao

AU - Zhao, Wanbing

AU - Lewis, Frank L.

AU - Jiang, Zhong Ping

AU - Modares, Hamidreza

N1 - 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: © 2020 Technical Committee on Control Theory, Chinese Association of Automation.

PY - 2020/7

Y1 - 2020/7

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85091397830&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85091397830&partnerID=8YFLogxK

U2 - 10.23919/CCC50068.2020.9188809

DO - 10.23919/CCC50068.2020.9188809

M3 - Conference contribution

AN - SCOPUS:85091397830

T3 - Chinese Control Conference, CCC

SP - 6816

EP - 6821

BT - Proceedings of the 39th Chinese Control Conference, CCC 2020

A2 - Fu, Jun

A2 - Sun, Jian

PB - IEEE Computer Society

T2 - 39th Chinese Control Conference, CCC 2020

Y2 - 27 July 2020 through 29 July 2020

ER -