Data-Driven Event-Triggered Predictive Control for Consensus of Discrete-Time Multi-Agent Systems with Time-Varying Delays

Consensus control for discrete-time multi-agent systems is increasingly complex when facing unknown dynamics and time-varying communication delays. Although data-driven control has emerged as a powerful tool to bypass model reliance, few existing studies simultaneously address the challenges of delay compensation and limited communication bandwidth. To bridge this gap, this paper proposes a novel data-driven event-triggered predictive control framework. We leverage Willems’ fundamental lemma to construct a predictive model directly from historical data, eliminating the need for system identification. Furthermore, an integrated event-triggered mechanism with a dynamic threshold updates control signals only when necessary, effectively reducing transmission load. Theoretical analysis confirms the recursive feasibility and stability of the closed-loop system, while simulations demonstrate that the proposed method achieves robust consensus with significantly reduced event-triggering frequency.