Consensus control for multi-agent systems with quasi-one-sided Lipschitz nonlinear dynamics via iterative learning algorithm

This paper deals with the problem of iterative learning control algorithm for consensus of a class of multi-agent systems, and all the agents in the considered systems are governed by the nonlinear dynamics with quasi-one-sided Lipschitz condition. Based on the framework of network topologies, distributed consensus-based iterative learning control protocols are designed by using the nearest neighbor knowledge. Under the action of the iterative learning control law, consensus on the finite time interval along the iteration axis can be reached for all the directed communication graphs with spanning trees. A simulation example is finally used to illustrate the effectiveness of the proposed approach.