Iterative Learning Control and feedforward for LPV systems: Applied to a position-dependent motion system

Abstract

Iterative Learning Control (ILC) enables performance improvement by learning from previous tasks. The aim of this paper is to develop an ILC approach for Linear Parameter Varying (LPV) systems to enable improved performance and increased convergence speed compared to the linear time-invariant approach. This is achieved through dedicated analysis and norm-optimal synthesis of LPV learning filters. Application to a position-dependent motion system shows a significant improvement in accuracy and convergence rate, thereby confirming the potential of the proposed approach.