B-Spline Network Based Iterative Learning for Tracking Control of a Piezoelectric Actuator

Abstract

This paper presents the trajectory tracking approach of a piezoelectric actuator using an iterative learning control (ILC) scheme based on B-spline network (BSN) filtering. The ILC scheme adopts a state compensated iterative learning formula, which compensates for the state difference between two consecutive iterations so that the iterative learning can learn from the tracking errors of the previous iteration effectively. The BSN is used to attenuate the noises and retrieve the signals of the tracking errors for ILC. The BSN serves as a unique filter which generally does not have zero-phase responses. Design details on the ILC scheme using the BSN filtering are discussed in the paper. Experiments of tracking two desired trajectories on a piezoelectric actuator are presented. Experimental results show that the state compensated ILC scheme using the BSN filtering can achieve fast error convergence and keep small steady-state tracking errors close to the system noise level. This research relaxes the restriction of the zero-phase criterion commonly applied to ILC filtering in the literature.