• DocumentCode
    3601142
  • Title

    Hierarchical Cooperative Control for Multiagent Systems With Switching Directed Topologies

  • Author

    Jianqiang Hu ; Jinde Cao

  • Author_Institution
    Key Lab. of Meas. & Control of CSE, Southeast Univ., Nanjing, China
  • Volume
    26
  • Issue
    10
  • fYear
    2015
  • Firstpage
    2453
  • Lastpage
    2463
  • Abstract
    The hierarchical cooperative control problem is concerned for a two-layer networked multiagent system under switching directed topologies. The group cooperative objective is to achieve finite-time formation control for the upper layer of leaders and containment control for the lower layer of followers. Two kinds of cooperative strategies, including centralized-distributed control and distributed-distributed control, are proposed for two types of switching laws: 1) random switching law with the dwell time and 2) Markov switching law with stationary distribution. Utilizing the state transition matrix methods and matrix measure techniques, some sufficient conditions are derived for asymptotical containment control and exponential almost sure containment control, respectively. Finally, some numerical examples are provided to demonstrate the effectiveness of the proposed control schemes.
  • Keywords
    Markov processes; distributed control; hierarchical systems; matrix algebra; multi-agent systems; multi-robot systems; topology; Markov switching law; asymptotical containment control; centralized-distributed control; distributed-distributed control; finite-time formation control; hierarchical cooperative control; matrix measure technique; random switching law; state transition matrix method; stationary distribution; switching directed topology; two-layer networked multiagent system; Markov processes; Multi-agent systems; Network topology; Protocols; Switches; Topology; Containment control; finite-time formation; multiagent system; switching topology; two-layer network; two-layer network.;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2014.2386858
  • Filename
    7008562