Sampled-data iterative learning control for a piezoelectric motor

Piezoelectric motor is widely used for motion control such as hard disk drives, AUV, and mechatronics. This work¹, presents a framework for the design and performance analysis of sampled-data iterative learning control (ILC) for piezoelectric motor. The design and analysis are carried out in the frequency domain. Monotonic convergence criterion is used in ILC design. Four ILC designs associated with different learning functions and filters are considered, namely, the P-type, D-type, D²-type and general filter. Criteria for the selection of each type are presented. In addition, a relationship is shown between the sampling-time selection and the ILC convergence. Theoretical work concludes with a guideline for the ILC designs. Further, based on the frequency-domain design tools, a successful experimental implementation on the piezoelectric motor is demonstrated.