Noise suppression in buffer-state iterative learning control, applied to a high precision wafer stage

Iterative learning control (ILC) has been proven to be very effective at suppressing repetitive errors in broad spectrum of applications. Ever increasing demands on performance in positioning systems have led to the need for more advanced control systems, like ILC, to achieve the desired performance. A major drawback of any ILC is that in general it will amplify any noise present in the measurement of the system which can lead to undesired loss of performance. In this paper such a basic ILC is compared to a buffer-state ILC with respect to the noise amplification, with application to a wafer stage. Advantages of the buffer-state based ILC design, over a basic ILC design in handling system noise are illustrated by measurements on a wafer stage, clearly showing the advantage of using full buffer-state feedback to optimise a combination of both the ILC convergence rate and the ILC noise amplification.