Optimal Tracking Control of Heterogeneous Multi-agent Systems with Switching Topology Via Actor-Critic Neural Networks

Zhinan Peng, Jiangping Hu, Bijoy K. Ghosh

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

1 Scopus citations

Abstract

In this paper, an optimal tracking control problem is solved for high-order heterogeneous multi-agent systems with time-varying interaction networks within the framework of reinforcement learning. First, the optimal tracking control problem is formulated as a leader-follower multi-agent system. Second, a policy iteration based adaptive dynamic programming (ADP) algorithm is proposed to compute the performance index and the control law. Furthermore, the convergence to the optimal solutions is analyzed for the proposed algorithm. Third, an actor-critic neural network is applied to approximate the iterative performance index function and the control law, which implement the policy iteration algorithm online without using the knowledge of the system dynamics. Finally, some simulation results are presented to demonstrate the proposed optimal tracking control strategy.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages7037-7042
Number of pages6
ISBN (Electronic)9789881563941
DOIs
StatePublished - Oct 5 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: Jul 25 2018Jul 27 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
CountryChina
CityWuhan
Period07/25/1807/27/18

Keywords

  • Actor-critic neural network
  • Adaptive dynamic programming
  • Multi-agent systems
  • Optimal tracking control

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  • Cite this

    Peng, Z., Hu, J., & Ghosh, B. K. (2018). Optimal Tracking Control of Heterogeneous Multi-agent Systems with Switching Topology Via Actor-Critic Neural Networks. In X. Chen, & Q. Zhao (Eds.), Proceedings of the 37th Chinese Control Conference, CCC 2018 (pp. 7037-7042). [8483268] (Chinese Control Conference, CCC; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.23919/ChiCC.2018.8483268