ILC-adapted parameter optimization of cross-coupled single-input fuzzy tracking controllers for an X-Y positioning table

Abstract

ABSTRACT This article systematically formulates an optimized cross-coupled single-input fuzzy-logic control mechanism that aims to enhance the tracking and contouring control performance of an X-Y positioning table. The motion along each axis is controlled via dedicated tracking controllers. The ubiquitous Iterative-Learning-Controller (ILC) is a standard tracking controller commonly used for high precision dual-axis systems. However, it puts recursive computational burden on the computer and its performance deteriorates under abrupt parametric variations. Conversely, a well-postulated proportional-derivative type single-input fuzzy-logic controller is robust. It offers faster transient recovery against disturbances, computational efficiency, and minimal tracking error in the response. In this article, the least-squared-regression-method is used to adaptively adjust the shapes of fuzzy membership-functions according to the control trajectory generated by ILC. This synergistic collaboration enables the fuzzy controller to effectively reject any external bounded disturbances in plant dynamics, while adapting to the convergence and precise tracking control behavior of ILC. The motion control system is also augmented with a single-input fuzzy cross-coupling controller that improves the contouring accuracy of the drive system by effectively attenuating the effects of axis mismatch. The efficacy of the proposed controller is validated via hardware-in-the-loop experiments, by plotting a circular contour using an X-Y position table.