Trackability Compensation for Iterative Learning Control: A Data-Based Approach

This paper aims at proposing a data-based trackability compensation strategy for iterative learning control systems to enhance their tracking performances when confronted with untrackable references. By designing and leveraging offline input-output test principles, an alternative data-based representation is constructed, based on which a data-based trackability criterion is developed. In scenarios where the reference outputs are untrackable, by interconnecting the original system with an auxiliary system, the trackability set of the interconnected system is modified. Consequently, the originally untrackable references become trackable for the interconnected system, and the perfect tracking preformances of iterative learning control can be guaranteed.