Adaptive Iterative Learning Control for Leader-following Multi-agent Systems with Unknown Control Directions under Directed Graph

In this paper, a leader following consensus problem for a first-order multi-agent system without priori knowledge of the control direction is studied by using adaptive iterative learning control. For the uncertainty of control direction, the Nussbaum-type gain function is proposed. The adaptive iterative learning control protocols are designed to achieve leader-following consensus under directed graph. The protocols ensure that all following agents can follow the leader agent perfectly within a finite interval as the number of iterations increases. Finally, an example proves the validity of the results.