Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach
Abstract
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration- and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.
Cite
@article{arxiv.2209.05155,
title = {Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach},
author = {Leontine Aarnoudse and Johan Kon and Koen Classens and Max van Meer and Maurice Poot and Paul Tacx and Nard Strijbosch and Tom Oomen},
journal= {arXiv preprint arXiv:2209.05155},
year = {2022}
}
Comments
To appear in Conference on Decision and Control 2022