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

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun Fu, Jian Sun
PublisherIEEE Computer Society
Pages6816-6821
Number of pages6
ISBN (Electronic)9789881563903
DOIs
StatePublished - Jul 2020
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: Jul 27 2020Jul 29 2020

Publication series

NameChinese Control Conference, CCC
Volume2020-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference39th Chinese Control Conference, CCC 2020
CountryChina
CityShenyang
Period7/27/207/29/20

ASJC Scopus subject areas

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

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  • 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). [9188809] (Chinese Control Conference, CCC; Vol. 2020-July). IEEE Computer Society. https://doi.org/10.23919/CCC50068.2020.9188809