Iterative learning control for multi‐agent systems with noninstantaneous impulsive consensus tracking

Abstract

AbstractIn this paper, we consider the consistent tracking problem of noninstantaneous impulsive multi‐agent systems. Take advantage of the repeatability of tracking tasks and the learning ability of each agent, we show that all agents of linear systems are driven to achieve a given asymptotical consensus as the number of iteration increases by using the standard ‐type learning law with the initial state learning rule. In addition, for nonlinear systems, we use ‐type learning law with the initial state learning rule to prove that all agents are driven to achieve a given asymptotical consensus as the number of iteration increases. Finally, two numerical examples are given to verify the effectiveness of our algorithm.