Improved Intersample Behaviour of Non-Minimum Phase Systems using State-Tracking Iterative Learning Control

Iterative learning control is well-proven technique to achieve perfect tracking performance for repetitive motion tasks. However, traditional output-tracking ILC focuses on perfect on-sample tracking, while oscillations often occur between the sampling instances. The aim of this paper is to reduce the intersample oscillations for the tracking control of minimum and non-minimum phase systems. A new ILC framework called statetracking ILC is successfully applied to a motion system. The state-tracking ILC achieves perfect state-tracking and is shown to reduce the intersample oscillations on fourth-order minimum and non-minimum phase motion systems compared to the outputtracking ILC.