Iterative Learning Heterogeneous Trajectory Tracking For Partially Interdependent Networks

Trajectory tracking is a hot topic in the field of complex systems. In this paper, based on the Lyapunov stability theory, combined with the adaptive iterative learning control method, the heterogeneous trajectory tracking of partially interdependent networks is investigated in a given finite time interval. The considered network consists of two subnets, where only the first m nodes are intercoupled. Firstly, some suitable iterative learning controllers, as well as the adaptive laws and the coupling strength learning laws, are designed for each node. Secondly, the boundedness and the convergence of the signals are proved using the proposed control strategy. Finally, some simulations are presented to illustrate the effectiveness and feasibility of the theoretical results presented in this paper. Generally, the accuracy trajectory tracking is found almost proportional to the number of iterations. When the number of iterations is fixed, with the increasing number of nodes having interdependence, the time required to track target accurately is reduced.