• DocumentCode
    175437
  • Title

    Stochastic consensus of linear multi-agent systems: Communication noises and Markovian switching topologies

  • Author

    Long Cheng ; Yunpeng Wang ; Zeng-Guang Hou ; Min Tan

  • Author_Institution
    State Key Lab. of Manage. an Control for Complex, Inst. of Autom., Beijing, China
  • fYear
    2014
  • fDate
    May 31 2014-June 2 2014
  • Firstpage
    274
  • Lastpage
    279
  • Abstract
    This paper studies the mean square and almost sure consensus of discrete-time linear multi-agent systems with communication noises under Markovian switching topologies. By a sophisticated stochastic-approximation type protocol, the closed-loop dynamics of this linear multi-agent system can be transformed into a discrete-time first-order integral multi-agent system. It is proved that if all roots of a polynomial, whose coefficients are the parameters in the gain vector of the proposed protocol, are in the unit circle, there is certain equivalence between the consensus of original linear multi-agent system and the consensus of transformed first-order integral multi-agent system. Then some sufficient conditions on the mean square/almost sure consensus of linear multi-agent systems can be obtained accordingly. Finally, theoretical analysis is verified by simulation examples.
  • Keywords
    Markov processes; closed loop systems; discrete time systems; linear systems; polynomials; robot dynamics; stochastic systems; vectors; Markovian switching topology; closed-loop dynamics; communication noise; discrete-time first-order integral multiagent system; discrete-time linear multiagent systems; gain vector; mean square; polynomial; stochastic consensus; stochastic-approximation type protocol; unit circle; Multi-agent systems; Noise; Protocols; Random variables; Stochastic processes; Switches; Topology; Consensus; Linear time-invariant dynamics; Markovian switching topology; Multi-agent systems; Stochastic noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (2014 CCDC), The 26th Chinese
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-3707-3
  • Type

    conf

  • DOI
    10.1109/CCDC.2014.6852158
  • Filename
    6852158