First order iterative learning control for a single axis piezostage system

Nowadays many machines and robots are programmed to perform the same task repeatedly. The Iterative Learning Control (ILC) paradigm is based on the idea that the performance of a system that executes the same trial multiple times can be improved by learning from the previous iterations. The objective of ILC is to improve the batch process performance by incorporating past trials error information into the control reference signal for the subsequent iteration. The ILC algorithms are categorized with respect to the number of past iterations considered to compute the next control signal and the first order ILC includes those algorithms considering only information about the last trial. In this paper different first order ILC update laws have been considered and compared controlling a Single-Input Single-Output (SISO) micro-positioning piezostage system. The proposed comparison allows to evaluate the performance of different first order ILC algorithms tested on the considered real world case study