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Exponential Bipartite Synchronization of Fractional-Order Multilayer Signed Networks via Hybrid Impulsive Control

  • Yao Xu
  • , Teng Lin
  • , Xinzhi Liu*
  • , Wenxue Li*
  • *Corresponding author for this work
  • Harbin Institute of Technology Weihai
  • University of Waterloo

Research output: Contribution to journalArticlepeer-review

Abstract

This article investigates the problem of exponential bipartite synchronization of fractional-order multilayer signed networks via hybrid impulsive control. First, the mathematical model of the networks is established by integrating the fractional dynamics of nodes, the multilayer network edges, and the positive and negative weights (antagonistic relationship), which is more pluralistic and practical. Second, a hybrid impulsive controller is designed, which is composed of the feedback control part and the impulsive control part to realize the exponential bipartite synchronization objective. Both positive and negative impulsive effects are considered and the ranges of the impulsive control gain related to the order of Caputo fractional derivative are discussed. In addition, some sufficient conditions for exponential bipartite synchronization of fractional-order multilayer signed networks are obtained by using the signed graph theory, Lyapunov method, and average impulsive interval method. Furthermore, in order to illustrate the practicability of the theoretical results, fractional-order coupled Chua's circuits model and fractional-order power systems built on multilayer signed networks are established and the exponential bipartite synchronization issues are analyzed. Numerical examples and simulations are provided to show the effectiveness of the obtained results.

Original languageEnglish
Pages (from-to)3926-3938
Number of pages13
JournalIEEE Transactions on Cybernetics
Volume53
Issue number6
DOIs
StatePublished - 1 Jun 2023
Externally publishedYes

Keywords

  • Exponential bipartite synchronization
  • fractional-order signed networks
  • hybrid impulsive control
  • multilayer networks

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