Skip to main navigation Skip to search Skip to main content

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

  • Hao Liu
  • , Wanbing Zhao
  • , Frank L. Lewis
  • , Zhong Ping Jiang
  • , Hamidreza Modares
  • Beihang University
  • University of Texas at Arlington
  • New York University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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
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: 27 Jul 202029 Jul 2020

Publication series

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

Conference

Conference39th Chinese Control Conference, CCC 2020
Country/TerritoryChina
CityShenyang
Period27/07/2029/07/20

Fingerprint

Dive into the research topics of 'Data-based Formation Control for Underactuated Quadrotor Team via Reinforcement Learning'. Together they form a unique fingerprint.

Cite this