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An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM.

Wugang Zhang1, Wei Guo2, Chuanwei Zhang3

  • 1School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China. zwg@xust.edu.cn.

Sensors (Basel, Switzerland)
|March 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new online calibration method for 2D galvanometers using wavelet kernel extreme learning machines (KELM). The proposed wavelet KELM method significantly improves accuracy and real-time performance for moving target position measurement.

Keywords:
online calibrationtwo-dimensional galvanometerwavelet kernel extreme learning machine

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Area of Science:

  • Robotics and Control Systems
  • Machine Learning Applications
  • Optical Engineering

Background:

  • Traditional online calibration methods for 2D galvanometers face challenges in achieving high precision and real-time performance.
  • Accurate calibration is crucial for moving target position measurement applications.

Purpose of the Study:

  • To develop a novel online calibration method for 2D galvanometers.
  • To enhance both the precision and real-time capabilities of calibration techniques.

Main Methods:

  • A system structure and experimental setup were established for online calibration.
  • A wavelet kernel extreme learning machine (KELM) algorithm was employed for calibration analysis.
  • Training data acquisition, testing, and verification methods were defined.

Main Results:

  • The wavelet KELM method demonstrated reduced root mean square errors (RMSE) by 16.4% and 38.6% compared to the original ELM.
  • Standard deviations (Sd) were reduced by 19.2% and 36.6%, indicating improved generalization and noise suppression.
  • Despite a slightly longer operation time than ELM, the wavelet KELM achieved superior real-time performance due to its kernel complexity.

Conclusions:

  • The proposed wavelet KELM online calibration method offers superior accuracy and real-time performance for 2D galvanometers.
  • This method effectively addresses the nonlinearities and noise in 2D galvanometer measurements.
  • The findings suggest a promising advancement for high-precision moving target tracking applications.