Multi-agent consensus tracking with input sharing by iterative learning control

This work addresses the multi-agent consensus tracking problem by iterative learning control (ILC) with input sharing. In many ILC works for multi-agent coordination problem, each agent maintains its own input learning, and the input signal is corrected by local measurements over iteration domain. If the agents are allowed to share their learned inputs among them, the strategy can improve the learning process as more learning resources are available. In this work, a new type of learning controller with input sharing is developed, and the convergence condition is rigorously derived and analyzed. It turns out that the traditional ILC law renders a special case of the developed controller. In the numerical study, the learning controller with input sharing demonstrates not only faster convergence but also smooth transient performance.