A Hybrid Inverse Model Algorithm for Linear Discrete Iterative Learning Control

This paper investigates iterative learning control for linear discrete systems. Under general conditions, an explicit formula for the inverse model-based algorithm is derived. Building on this general formula, a class of hybrid inverse-model algorithms is developed, which drives the tracking error to zero after a finite number of iterations while maintaining an approximately minimal error during those iterations. Moreover, the proposed algorithms provide a framework for integrating other control strategies. Finally, numerical simulations confirm the effectiveness of the proposed approach.