Commutation-Angle Iterative Learning Control for Intermittent Data: Enhancing Piezo-Stepper Actuator Waveforms

TL;DR

This paper introduces a novel commutation-angle iterative learning control method to improve the performance of piezo-stepper actuators by effectively attenuating disturbance effects caused by their walking motion.

Contribution

It develops a new iterative learning control approach tailored for the commutation-angle domain, addressing iteration-varying and non-equidistant sampling issues.

Findings

Significant reduction in disturbance effects on the actuator.

Enhanced positioning accuracy demonstrated experimentally.

Framework effective across varying drive frequencies.

Abstract

Piezo-stepper actuators are used in many nanopositioning systems due to their high resolution, high stiffness, fast response, and the ability to position a mover over an infinite stroke by means of motion reminiscent of walking. The aim of this paper is to develop a control approach for attenuating disturbances that are caused by the walking motion and are therefore repeating in the commutation-angle domain. A new iterative learning control approach is developed for the commutation-angle domain, that addresses the iteration-varying and non-equidistant sampling that occurs when the piezo-stepper actuator is driven at varying drive frequencies by parameterizing the input and error signals. Experimental validation of the framework on a piezo-stepper actuator leads to significant performance improvements.