Cooperative optimal output regulation of multi-agent systems using adaptive dynamic programming

Weinan Gao, Zhong Ping Jiang, Frank L. Lewis, Yebin Wang

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

Abstract

This paper proposes a novel solution to the adaptive optimal output regulation problem of continuous-time linear multi-agent systems. A key strategy is to resort to reinforcement learning and approximate/adaptive dynamic programming. A data-driven, non-model-based algorithm is given to design a distributed adaptive suboptimal output regulator in the presence of unknown system dynamics. The effectiveness of the proposed computational control algorithm is demonstrated via cooperative adaptive cruise control of connected and autonomous vehicles.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2674-2679
Number of pages6
ISBN (Electronic)9781509059928
DOIs
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2017 American Control Conference, ACC 2017
CountryUnited States
CitySeattle
Period5/24/175/26/17

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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