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Robust distributed consensus tracking for stochastic linear multi-agent systems under directed switching topologies

  • Nanyang Technological University
  • Southeast University, Nanjing

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

Abstract

This paper investigates a robust mean-square exponential consensus tracking problem for a class of Itô stochastic linear multi-agent systems in the presence of stochastic noises and directed switching topologies. The desired tracking trajectory is only available to a small group of followers. By only using the relative state information among the neighbors and themselves, a distributed consensus tracking protocol is designed to achieve robust consensus tracking. A multi-step design procedure is developed for designing the feedback controller gain matrix in the protocol. Based on an average dwell time (ADT) approach, sufficient conditions on robust mean-square exponential consensus tracking are derived via the tools from M-matrix theory, graph theory, Lyapunov function analysis, and Itô stochastic theory. Numerical simulation is provided to illustrate the effectiveness of the theoretical result.

Original languageEnglish
Title of host publication11th IEEE International Conference on Control and Automation, IEEE ICCA 2014
PublisherIEEE Computer Society
Pages174-179
Number of pages6
ISBN (Print)9781479928378
DOIs
StatePublished - 2014
Externally publishedYes
Event11th IEEE International Conference on Control and Automation, IEEE ICCA 2014 - Taichung, Taiwan, Province of China
Duration: 18 Jun 201420 Jun 2014

Publication series

NameIEEE International Conference on Control and Automation, ICCA
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Conference

Conference11th IEEE International Conference on Control and Automation, IEEE ICCA 2014
Country/TerritoryTaiwan, Province of China
CityTaichung
Period18/06/1420/06/14

Keywords

  • Directed switching topology
  • Distributed consensus tracking
  • Mean-square exponential convergence
  • Stochastic linear multi-agent system

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