• DocumentCode
    11708
  • Title

    Sampled-Data Exponential Synchronization of Complex Dynamical Networks With Time-Varying Coupling Delay

  • Author

    Zheng-Guang Wu ; Peng Shi ; Hongye Su ; Jian Chu

  • Author_Institution
    Nat. Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
  • Volume
    24
  • Issue
    8
  • fYear
    2013
  • fDate
    Aug. 2013
  • Firstpage
    1177
  • Lastpage
    1187
  • Abstract
    This paper studies the problem of sampled-data exponential synchronization of complex dynamical networks (CDNs) with time-varying coupling delay and uncertain sampling. By combining the time-dependent Lyapunov functional approach and convex combination technique, a criterion is derived to ensure the exponential stability of the error dynamics, which fully utilizes the available information about the actual sampling pattern. Based on the derived condition, the design method of the desired sampled-data controllers is proposed to make the CDNs exponentially synchronized and obtain a lower-bound estimation of the largest sampling interval. Simulation examples demonstrate that the presented method can significantly reduce the conservatism of the existing results, and lead to wider applications.
  • Keywords
    Lyapunov methods; asymptotic stability; complex networks; control system synthesis; convex programming; delays; functional equations; sampled data systems; sampling methods; synchronisation; time-varying systems; uncertain systems; complex dynamical networks; conservatism reduction; convex combination technique; error dynamics; exponential stability; exponentially synchronized CDN; lower-bound estimation; sampled-data controller design method; sampled-data exponential synchronization; sampling interval; sampling pattern; time-dependent Lyapunov functional approach; time-varying coupling delay; uncertain sampling; Complex dynamicalnetworks (CDNs); exponential synchronization; sampled-data control; time-varying coupling delay;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2013.2253122
  • Filename
    6495479