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Time-varying formation tracking for general linear multi-agent systems with multiple leaders and input saturation

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

Abstract

This paper studies the time-varying formation tracking problem for general linear multi-agent systems, in which there exist multiple leaders and the control input of each agent is bounded. Followers are classified into well-informed ones and uninformed ones in order to determine the unique convex combination of states of leaders as the formation reference. Time-varying formation of followers is predefined by a continuous differential offset vector with respect to the formation reference. A distributed formation tracking protocol is constructed using neighboring information and a low-gain feedback method is adopted to deal with input saturation. Sufficient conditions for the multi-agent system to achieve time-varying formation tracking are derived as well as the formation feasibility constraint. A bisection method is further proposed to determine the feasible low gain parameter without using global information. Finally, a numerical example is presented to verify the effectiveness of the theoretical results.

Original languageEnglish
Title of host publicationProceedings - 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages202-207
Number of pages6
ISBN (Electronic)9781538672556
DOIs
StatePublished - 6 Jul 2018
Event33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018 - Nanjing, China
Duration: 18 May 201820 May 2018

Publication series

NameProceedings - 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018

Conference

Conference33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018
Country/TerritoryChina
CityNanjing
Period18/05/1820/05/18

Keywords

  • Input saturation
  • Low-gain feedback
  • Multiple leaders
  • Time-varying formation tracking

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