Attitude synchronization for multiple quadrotors using reinforcement learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, a reinforcement learning based control law is proposed to solve the attitude synchronization problem of the leader-following multi-quadrotor systems. The overall system is composed of a team of quadrotors, modeled with highly nonlinear and coupled dynamics. An optimal control solution is obtained by solving an augmented Hamilton-Jacobi-Bellman equation. A reinforcement learning approach is used to learn the optimal control law. Simulation results are provided to verify the effectiveness of the proposed controller.

Original languageEnglish (US)
Title of host publicationProceedings of the 38th Chinese Control Conference, CCC 2019
EditorsMinyue Fu, Jian Sun
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9789881563972
StatePublished - Jul 2019
Event38th Chinese Control Conference, CCC 2019 - Guangzhou, China
Duration: Jul 27 2019Jul 30 2019

Publication series

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


Conference38th Chinese Control Conference, CCC 2019

ASJC Scopus subject areas

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


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