Iterative Learning Control for Nonlinear Multi-Agent Systems With Initial Shifts

In this paper, a discussion is made on the consensus tracking control by iterative learning method for high-order nonlinear multi-agent systems. Among them, all agents with initial state errors are enabled to perform a given repetitive task over a finite interval. The method proposed can achieve consensus tracking through a series of initial shifts correction actions. In the process of tracking, this algorithm rectifies the initial error of the state
$x_{n}$
of each agent at first, then the error of
$x_{n-1}$
, and so on. All of these rectifying actions are finished in a specified interval. Furthermore, the algorithm has shown effective in the improvement of tracking performance through simulation.