Nonlinear iterative learning control applied to an aerostatic X–Y planar motion stage

Abstract

The planar motion stage with multiple degrees of freedom, positioning in large travel ranges, has been widely used in high-accuracy applications such as semiconductor fabrication equipment and advanced scientific instruments. To achieve precision motion control, iterative learning control has been regarded as an effective means. However, linear iterative learning control techniques attenuate recurring disturbances while amplifying the non-recurring, suffering a fixed trade-off between convergence rates and noise amplification. In the present paper, an amplitude-based nonlinear iterative learning control is proposed with learning gain continuously updated to improve the control performance of the planar motion stage. For error levels beyond a predefined threshold, additional learning gain will be effectively used to diminish the low-frequency tracking error. Below the threshold, the original low gain value is maintained to avoid high-frequency noise amplification. Performance assessment on the developed non-contact planar motion stage shows that the amplitude-based nonlinear iterative learning strategy can realize a remarkable performance which includes micrometer positioning over large travel ranges, and provides a more desirable means to deal with the convergence rate and noise amplification.