B-spline network-based iterative learning control for trajectory tracking of a piezoelectric actuator

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 in order 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 the ILC. The BSN serves as a unique filter which generally does not have zero-phase responses. Design details on the ILC scheme using BSN filtering are discussed in the paper. Extensive experiments of tracking two desired trajectories for a piezoelectric actuator are presented. The experimental results show that the state-compensated ILC scheme using BSN filtering can achieve fast error convergence and keep small steady-state tracking errors close to the system noise level. This research thus relaxes the restriction of the zero-phase criterion commonly applied to the ILC filtering in the literature.