Adaptive iterative learning consensus control for nonlinear multi-agent systems with triggering state signals

This paper investigates the output consensus problem for a class of nonlinear multi-agent systems (MASs) under an adaptive iterative learning control (AILC) framework. In order to relax the requirements of the initial state in the iterative process, the expected consensus error trajectory is designed in advance. Moreover, considering that the controller gain function may cause singular value problems during the process of controller design, a new integral Lyapunov function is constructed. In addition, with the purpose of improving the usage of resources, a dynamic event-triggered mechanism based on state signals is proposed. This paper effectively solves the problem of non-differentiability of virtual controllers designed based on the backstepping method using intermittently transmitted triggering states. On this basis, an adaptive iterative learning consensus tracking control strategy for MASs based on event-triggered mechanisms is proposed. Finally, simulation examples are conducted to confirm the theoretical analysis.