Distributed point-to-point iterative learning control for nonlinear multi-agent systems

In this article, the distributed point-to-point iterative learning control (ILC) is proposed for the nonlinear multi-agent system, which has not been studied yet. It is proved that zero convergence of point-to-point consenus tracking error is achieved. Besides, the bounded convergence of point-to-point consensus tracking error is realized under bounded disturbance. In the case of output quantization, the proposed algorithm can achieve zero convergence of point-to-point consensus tracking error. Finally, the effectiveness of the proposed algorithm is verified by simulation.