Deep Learning-based Precision Control for Six-axis Compliant Nanopositioner
Refereed conference paper presented and published in conference proceedings

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AbstractIn this work, we present a new control method for compliant mechanisms based on deep learning models that achieve 100 nm precision in multiple axes with low-lost strain sensor arrays. In our setup, the strain sensor array is applied to the flexing components on a custom-designed compliant six-axis nanopositioner such that it simultaneously measures the mechanical strain (i.e., displacement) and mode shapes for real-time control, i.e., the deep model can learn, predict, and control the motion of the nanopositioner as a time sequence or 2-D images. During the training stage, capacitance sensors are used as references to minimize position errors; the number and location of sensor arrays can be optimized via the deep learning model. In the experiments, we demonstrate that the deep model developed based on the reinforcement learning method [1] can fully replace the classical PID control, realizing 100 nm precision without tuning any control parameters.
Acceptance Date01/08/2019
All Author(s) ListXiangbo Liu, Chenglin Li, Hongsheng Li, Xiaogang Wang, Shih-Chi Chen
Name of ConferenceAnnual Meeting of the American Society for Precision Engineering
Start Date of Conference28/10/2019
End Date of Conference01/11/2019
Place of ConferencePittsburgh, Pennsylvania
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the 34th ASPE Annual Meeting
Place of PublicationUSA
Pages139 - 144
LanguagesEnglish-United States
Keywordsdeep learning, precision control

Last updated on 2020-02-09 at 01:39