Iterative tuning notch filter for suppressing resonance in ultra-precision motion control

Abstract

In this article, a practical iterative algorithm for tuning the parameters of notch filter is presented to suppress highfrequency resonance in ultra-precision motion control. Notch filter is a useful tool to suppress middle-and highfrequency resonance to improve control precision. The traditional tuning method for it depends on the Fourier transform of the positioning error or the modal analysis of the motion stage, which cannot get the optimal control performance. The proposed algorithm can be used to tune the parameters of notch filter iteratively through minimizing a cost function of the positioning error, and it needs only measurement signals in actual motion system rather than detailed model of the motion stage. The proposed algorithm can suppress mechanical resonance and meanwhile minimize positioning error, which are demonstrated by experimental results in wafer stage of photolithography.

Design of a run‐to‐run controller based on discrete sliding‐mode observer

“…In order to eliminate disturbance and maintain the small CD, many different kinds of algorithms have been presented, such as the laser direct writing method 1 . An ultra‐precision motion control algorithm has been developed to obtain the high positioning precision of the step‐and‐scan projection photolithography 2 . To meet the requirement for precise overlay, a multifield sub‐5 nm overlay algorithm has been proposed in imprint lithography 3 .…”

Force ripple compensation of the directly-driven linear motors via iterative tuning feed-forward controller

“…Hence, the iteratively tuning method has the advantage that it can guarantee both the performance and robustness in theory. It is found that this method has succeeded in tuning the parameters of many types of controllers, such as notch filters, 27 feedback controllers, 28 and admittance controllers. 29 When it is used to tune the model-based feed-forward controller, it can make the tracking error converge to zero for various trajectories because the controller has good extrapolation capabilities to the commands.…”