PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 12, 2026International Journal of Robust and Nonlinear Control1 citations

Stochastic Bounded Consensus of Multi‐Agent Systems Regarding Fractional Brownian Motions Through Event‐Triggered Control

View Full Paper
JXJing XieHFHuihui FengBPBiao Peng

Key Points

  • The aim is to explore stochastically bounded consensus in multi-agent systems influenced by fractional Brownian motions.
  • Developed event-triggered control protocols to minimize communication needs.
  • Constructed a double-integral Lyapunov functional related to the Hurst exponent for analysis.
  • Applied Schur complement lemma and matrix inequalities to derive the consensus conditions.
  • Designed parameters using linear matrix inequalities.
  • Proposed a method for achieving stochastically bounded consensus under disturbances.
  • Demonstrated effective event-triggered control for reduced communication and resource usage.
  • Illustrated the approach through a practical example showing effectiveness.

Abstract

ABSTRACT Under fractional Brownian motion disturbances and semi‐Markovian switching topologies, the problem of stochastically bounded consensus is investigated for multi‐agent systems via the event‐triggered control method. For the purpose of saving communication resources, we propose an event‐triggered switching control protocol with respect to the lower bound of each sampling interval and the semi‐Markov process. To deal with fractional Brownian motion disturbances for less conservativeness, a new double‐integral Lyapunov functional is constructed with relation to the Hurst exponent. Utilizing the Schur complement lemma and some other matrix inequalities, the stochastically event‐triggered bounded consensus is proposed. Then the event‐triggering parameters and controller parameters are designed by linear matrix inequalities. Finally, an example is addressed for illustrating the effectiveness of our findings.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Xie et al. (2026) studied this question.

synapsesocial.com/papers/698d6e5a5be6419ac0d53fabhttps://doi.org/10.1002/rnc.70424
Ask AI
Helpful
Bookmark
Share
View Full Paper