Journal
IEEE-ASME TRANSACTIONS ON MECHATRONICS
Volume 26, Issue 4, Pages 2127-2138Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2020.3032565
Keywords
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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Funding
- University of Texas at Dallas
- U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office Award [DE-EE0008322]
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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.
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.
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