An iteration-domain filter for controlling transient growth in Iterative Learning Control

Transient growth is a problem in Iterative Learning Control (ILC) in which the tracking error temporarily grows very large during the learning process, before converging to a small value. While some ILC algorithms can guarantee monotonic convergence, there are limitations when the model is uncertain. This paper presents a new algorithm to reduce the transient growth in ILC. An iteration domain filter, which can be applied to any linear ILC system, is proposed. The filter slows the learning process, in a controlled manner, to limit transient growth. Fundamental results relating the learning process convergence rate to explicit bounds on the transient growth are presented. Two examples that demonstrate the effectiveness of the method are presented: one in SISO design and one in network design.