Comparative study of standard and norm-optimal iterative learning control

This paper investigates the relationship between standard and norm-optimal Iterative Learning Control (ILC) methods, focusing on their synthesis principles and practical implementation. The study is carried out on a motion system comprising a walking beam and a robotic mechanism coupled via a flexible link. The main contribution lies in demonstrating a general equivalence between the norm-optimal ILC algorithm and a family of standard ILC algorithms whose model coefficients vary with each sampling time. This result provides new insights into the fundamental connection between these two classes of ILC methods. It is also shown that the norm-optimal ILC algorithm significantly outperforms standard ILC approaches based on dynamic model inversion. Simulation results confirm its superior tracking accuracy and robustness in the examined motion system.This work establishes a novel structural equivalence between norm-optimal and standard ILC algorithms, providing both theoretical insight and practical implementation guidance. The proposed formulation enables unification of these two paradigms and facilitates hybrid design strategies with improved performance and lower implementation complexity.