Iterative Learning Control for Video-Rate Atomic Force Microscopy

We present a control scheme for video-rate atomic force microscopy with rosette pattern. The controller structure involves a feedback internal-model-based controller and a feedforward iterative learning controller. The iterative learning controller is designed to improve tracking performance of the feedback-controlled scanner by rejecting the repetitive disturbances arising from the system nonlinearities. We investigate the performance of two inversion techniques for constructing the learning filter. We conduct tracking experiments using a two-degree-of-freedom microelectromechanical system (MEMS) nanopositioner at frame rates ranging from 5 to 20 frames per second. The results reveal that the algorithm converges rapidly and the iterative learning controller significantly reduces both the transient and steady-state tracking errors. We acquire and report a series of high-resolution time-lapsed video-rate AFM images with the rosette pattern.

Automated summary

A control scheme for video-rate atomic force microscopy with rosette pattern is proposed, utilizing a feedback internal-model-based controller and a feedforward iterative learning controller to enhance tracking performance and reject repetitive disturbances. Two inversion techniques for constructing the learning filter are investigated, showing that the iterative learning controller significantly reduces both transient and steady-state tracking errors. High-resolution time-lapsed video-rate AFM images with the rosette pattern are successfully acquired and reported.