Norm optimal Cross-Coupled Iterative Learning Control

In this paper, we focus on improving contour tracking in precision motion control (PMC) applications through the use of Cross-Coupled Iterative Learning Control (CCILC). Initially, the relationship between individual axis errors and contour error is discussed, including insights into the different reasons for implementing CCILC versus individual axis ILC. A Norm Optimal (N.O.) framework is used to design optimal learning filters based on design objectives. The general N.O. framework is reformatted to include the contour error, as well as individual axis errors. General guidelines for tuning the different weighting matrices are presented. The weighting approach of this framework enables one to focus on individual axis or contour tracking independently. The performance benefits of N.O. CCILC versus ILC are illustrated through simulation and experimental testing on a multi-axis robotic testbed.