Iterative learning control for multi-agent systems with impulsive consensus tracking - Cao, Xiaokai; Fečkan, Michal; Shen, Dong; Wang, JinRong (Nonlinear Analysis: Modelling and Control, 2021)
In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory.