Event-Triggered Bipartite Consensus for Fuzzy Multi-Agent Systems under Markovian Switching Signed Topology

Jiafeng Yu, Choon Ki Ahn, Peng Shi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this paper, we study the problems of bipartite and cooperative consensus with a strictly dissipative performance for fuzzy multi-agent systems (MASs) in a unified framework. First, we prove that bipartite consensus over a structurally balanced signed graph is equivalent to cooperative consensus over the corresponding unsigned graph by leveraging the gauge transformation for a class of nonlinear MASs. Then, a polynomial fuzzy model is constructed to describe the nonlinear MAS formed by one leader and followers. For mitigating communication and computational load, a mode-dependent eventtriggered transmission strategy is proposed. By establishing the switching topologies through Markovian process, a new sampleddata event-triggered consensus protocol is designed.With a modedependent Lyapunov-Krasovskii functional, a novel relaxed dissipative criterion is obtained. The criterion guarantees that all agents can achieve both event-triggered cooperative consensus and event-triggered bipartite consensus with the same magnitude but opposite signs for MASs over structurally balanced signed directed graphs and Markovian switching topologies. Moreover, the event-triggered parameters and consensus control gains can be numerically solved via the sum of squares (SOS) method. Simulation results are given to show the effectiveness of the proposed design method.

Original languageEnglish
JournalIEEE Transactions on Fuzzy Systems
Publication statusAccepted/In press - 2021


  • Consensus protocol
  • event-triggered control
  • Fuzzy modeling
  • Laplace equations
  • Marko-vian switching topology
  • multi-agent system
  • Multi-agent systems
  • Numerical models
  • Simulation
  • strict dissipativity
  • Switches
  • Topology

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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