• DocumentCode
    1764940
  • Title

    Consensus in Continuous-Time Multiagent Systems Under Discontinuous Nonlinear Protocols

  • Author

    Bo Liu ; Wenlian Lu ; Tianping Chen

  • Author_Institution
    Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi´an, China
  • Volume
    26
  • Issue
    2
  • fYear
    2015
  • fDate
    Feb. 2015
  • Firstpage
    290
  • Lastpage
    301
  • Abstract
    In this paper, we provide a theoretical analysis for nonlinear discontinuous consensus protocols in networks of multiagents over weighted directed graphs. By integrating the analytic tools from nonsmooth stability analysis and graph theory, we investigate networks with both fixed topology and randomly switching topology. For networks with a fixed topology, we provide a sufficient and necessary condition for asymptotic consensus, and the consensus value can be explicitly calculated. As to networks with switching topologies, we provide a sufficient condition for the network to realize consensus almost surely. In particular, we consider the case that the switching sequence is independent and identically distributed. As applications of the theoretical results, we introduce a generalized blinking model and show that consensus can be realized almost surely under the proposed protocols. Numerical simulations are also provided to illustrate the theoretical results.
  • Keywords
    continuous time systems; directed graphs; multi-agent systems; stability; topology; asymptotic consensus; consensus value; continuous-time multiagent systems; fixed topology; generalized blinking model; graph theory; nonlinear discontinuous consensus protocols; nonsmooth stability analysis; numerical simulations; randomly switching topology; switching sequence; switching topologies; weighted directed graphs; Convergence; Laplace equations; Multi-agent systems; Network topology; Protocols; Switches; Topology; Almost sure convergence; consensus; discontinuous; multiagent systems; switching; switching.;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2014.2314699
  • Filename
    6809213