Norm Optimal Iterative Learning Control: A Data-Driven Approach

Iterative learning control (ILC) is a design method that can improve the tracking performance for systems working in repetitive manner by from previous iterations. Norm optimal ILC well known with appealing convergence properties, e.g. monotonic error norm convergence. However, it requires an explicit system model design, which be difficult or expensive to obtain practice. To address this problem, paper proposes data-driven exploiting recent development control. A receding horizon implementation of further developed relax requirement on data. Convergence properties are analysed rigorously and simulation examples presented demonstrate effectiveness method.