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Iterative Learning Control for Video-Rate Atomic Force Microscopy
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DOI：10.1109/TMECH.2020.3032565.png)
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摘要
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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.
Keyword:
Nanopositioning
Force
Adaptive control
Iterative learning control
Atomic force microscopy
Internal model principle
iterative learning control (ILC)
microelectromechanical system (MEMS) nanopositioner
nonraster scanning
rosette pattern
video-rate atomic force microscopy (AFM)
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5.4K
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