Iterative Learning Control with High-Order Internal Model for Singular Multiagent Systems

In this paper, the problem of iterative learning consensus control is studied for singular multi agent systems with the iteration-varying desired trajectories described by a high-order internal model (HOIM). Utilizing the equivalent constraint decomposition form of the singular system, we generalize and apply the P-type ILC algorithm based on the Higher Order Iterative Methods (HOIM) to achieve consensus control of the singular multi agent systems with iteration-varying desired trajectories. Additionally, we provide the corresponding convergence conditions. Theoretical analyses show that the output of the singular multi agent systems under the action of the algorithm can achieve complete consensus tracking for the iteration-varying desired trajectories over the whole time interval. Finally, a numerical example was used to verify the correctness of the main results and the effectiveness of the methodology.