Enhanced iterative learning control for a piezoelectric actuator system using wavelet transform filtering

For trajectory tracking of a piezoelectric actuator system, an enhanced iterative learning control (ILC) scheme based on wavelet transform filtering (WTF) is proposed in this research. The enhanced ILC scheme incorporates a state compensation in the ILC formula. Combining state compensation with iterative learning, the scheme enhances tracking accuracies substantially, in comparison to the conventional D-type ILC and a proportional control-aided D-type ILC. The wavelet transform is adopted to filter learnable tracking errors without phase shift. Based on both a time–frequency analysis of tracking errors and a convergence bandwidth analysis of ILC, a two-level WTF is chosen for ILC in this study. The enhanced ILC scheme using WTF was applied to track two desired trajectories, one with a single frequency and the other with multiple frequencies, respectively. Experimental results validate the efficacy of the enhanced ILC in terms of the speed of convergence and the level of long-term tracking errors.