Consensus control of nonlinear stochastic multi-agent systems via hybrid event-triggered impulsive strategy

Abstract

This paper develops a distributed edge-based event-triggered impulsive control approach for mean-square consensus of nonlinear stochastic multi-agent systems. A continuously monitored triggering condition with time regularization is proposed as a key contribution. It effectively excludes Zeno behavior without requiring strict assumptions on system dynamics. The proposed strategy integrates impulsive control theory with stochastic stability analysis, significantly reducing the frequency of controller updates and inter-agent communications. This improves resource efficiency and enhances applicability in real-world stochastic environments. Notably, a novel convergence framework is employed for the consensus analysis. Although based on a Lyapunov-type functional, this framework follows a distinct analytical path from classical continuous-time Lyapunov theorems, yielding more flexible sufficient conditions for mean-square consensus. The theoretical results are validated through numerical simulations with nonlinear agent dynamics. These simulations confirm that the proposed protocol successfully achieves consensus while lowering communication costs and improving energy efficiency compared to existing event-triggered approaches.