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

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