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