Robust Iterative Learning Control for Multi-Agent Systems with Random Impulsive Disturbances

This work addresses the issue of robust iterative learning tracking for heterogeneous multi-agent systems in the presence of random impulsive disturbances. Unlike existing literature focusing solely on bounded deterministic impulsive perturbations, this work explicitly models the stochastic nature of practically prevalent random impulses, which emerge randomly in the iteration domain and impair tracking performance. A key mathematical observation is that these persistent non-repetitive random impulses preclude asymptotic zero convergence of the tracking error. To tackle this fundamental limitation, a distributed iterative learning control strategy is proposed to realize robust ultimate bounded tracking. We establish sufficient conditions that guarantee the ultimate boundedness of the tracking error in expectation by applying the Gronwall inequality and mathematical analysis tools. The derived theoretical results generalize the classical convergence criteria developed under deterministic impulsive disturbance frameworks. Finally, numerical simulations are conducted to verify the effectiveness of the established theoretical results.