Precision sinusoidal tracking for galvanometer scanner with smith predictor-based adaptive sliding mode control

Wei Chih Liu, Da Wei Liu, Jim Wei Wu, Kuang Yao Chang, Meng Hao Chou, Li Chen Fu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Galvanometer scanner (GVS) is a versatile instrument in the modern engineering fields. One of the important applications is to realize the laser scanning confocal microscope. Such inspection tool usually needs an affine signal to be its scanning trajectory like triangular waveform or sinusoidal signal. However, there still exists some problems such as temperature variation and time delay in GVS system, and thus the tracking performance of GVS will be jeopardized. In this paper, we design a smith predictor-based adaptive sliding mode control combined with an internal model principle (IMP) to address those limitations. With the proposed control method, it can simultaneously handle the mismatch model problem, which often occurs in Smith predictor control, as well as the issue of tracking the sinusoidal reference signals. A series of comprehensive experiments have been conducted, which demonstrate a better tracking accuracy as compared to the performances of those which only apply a traditional PD controller.

Original languageEnglish
Title of host publication2016 International Automatic Control Conference, CACS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages100-105
Number of pages6
ISBN (Electronic)9781509041084
DOIs
StatePublished - 10 Jul 2017
Event2016 International Automatic Control Conference, CACS 2016 - Taichung, Taiwan
Duration: 9 Nov 201611 Nov 2016

Publication series

Name2016 International Automatic Control Conference, CACS 2016

Conference

Conference2016 International Automatic Control Conference, CACS 2016
Country/TerritoryTaiwan
CityTaichung
Period9/11/1611/11/16

Keywords

  • Adaptive sliding mode control
  • Galvanometer scanner (GVS)
  • High-precision tracking
  • Internal model principle (IMP)
  • Smith predictor

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