Consensus tracking iterative learning control of second-order multi-agent systems

In this paper, the problem of consensus tracking control for a class of second-order leader-following nonparametric uncertain multi-agent systems, which perform a given repetitive task over a finite interval with arbitrary initial error. By means of learning control and initial shift rectifying, a first-order attractor control algorithm is presented.In the tracking process, the proposed algorithm simultaneously rectifies all the initial state shifts, and after enough iterations, the all following multi-agents' states perfectly track the leader's state in the preset time interval. Finally, simulation results demonstrate the effectiveness of the learning control algorithm.