Abstract
This paper applies the Iterative Learning Control (ILC) strategy in order to increase the performance of spiral patterns tracking using simple proportional-integral controllers. Such patterns arise in different areas where fast and smooth reference signals are required, as for example the Atomic Force Microscopy. The ILC is implemented as a feedforward control strategy that makes use of the information obtained in previous batches in order to improve the tracking performance, normally applied along side an already implemented feedback controller. A new suitable scanning reference pattern is also proposed, which allows the application of the iterative learning concept in repetitive tasks. The proposed control strategy is evaluated through a set of simulations using a numerical model of an Atomic Force Microscope nanopositioner. The numerical results obtained through the simulations show that the proposed ILC structure is able to improve the performance of a traditional proportional-integral controller available in the area.
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