Development of a hybrid iterative learning control for contouring NURBS curves
Journal
Asian Journal of Control
Journal Volume
13
Journal Issue
1
Pages
107-125
Date Issued
2011
Author(s)
Abstract
Abstract This paper develops a hybrid iterative learning control (ILC) algorithm which uses both tracking and contour errors as correction signals during the learning process. The tracking, contour and combined ILCs are first introduced and analyzed. A hybrid ILC is then proposed to predict contour errors based on the identified models and then used to determine whether the tracking or contour errors should be utilized for the correction signals. The criteria for selecting different signals are based on the predicted root mean square (RMS) value of the contour errors for non‐uniform rational B‐spline (NURBS) curves. Simulations performed on a butterfly trajectory represented by NURBS curves show that the hybrid ILC adopts tracking errors as correction signals for the first five iterations, even if the contour error RMS values are used as the objective function. As the iterations evolve, the hybrid ILC switches from using tracking errors to using contour errors as correction signals. Geometric interpretations are given to illustrate the switching behavior. It is shown that the hybrid ILC can significantly reduce contour errors as compared to the process without learning. Finally, validation experiments using the butterfly curve show that the hybrid ILC outperforms the tracking, contour and combined ILCs algorithms. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
Type
journal article