State Consistency Tracking for a Class of Singular Multi-agent System Based on Iterative Learning Method

In this paper, the iterative learning method is used to study the state consistency tracking problem of a class of singular multi-agent system with fixed initial deviation. Based on the equivalent constraint decomposition form of singular multi-agent systems, an iterative learning control algorithm is proposed. The results show that the state tracking error decreases with time when the initial state deviation exists. In order to further eliminate the influence of initial state deviation, an iterative learning control algorithm with initial correction strategy is proposed to realize the complete tracking of the state to the target in a certain period of time. Finally, simulation example shows the effectiveness of the algorithm.