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SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos

Wei Han1, Xiongzhu Bu2, Yihan Cao3

  • 1Nanjing University of Science and Technology, School of Mechanical Engineering, 200 Xiaolingwei, 210094 Nanjing, China. hanwei1114@njust.edu.cn.

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Summary
This summary is machine-generated.

This study analyzes gyroscopic effects on surface acoustic wave (SAW) torque sensors at high speeds. A novel Chaos Estimation of Distributed Algorithm-Least Squares Support Vector Machine (CEDA-LSSVM) method significantly reduces torque calculation errors.

Keywords:
CEDA-LSSVMSAWgyroscopic effect compensationtorque sensor

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Area of Science:

  • Mechanical Engineering
  • Sensor Technology
  • Signal Processing

Background:

  • High-speed rotation introduces gyroscopic effects, interfering with Surface Acoustic Wave (SAW) torque sensor accuracy.
  • Existing methods struggle to effectively compensate for this rotational interference.

Purpose of the Study:

  • To analyze the gyroscopic effect on SAW torque sensors during high-speed rotation.
  • To develop and validate a compensation method for reducing torque calculation errors.
  • To improve the performance of compensation algorithms for practical engineering applications.

Main Methods:

  • Deduction of SAW coupled equations incorporating torque and rotation loads.
  • Design and implementation of a SAW gyroscopic effect testing platform and turntable experiments.
  • Compensation using Multivariate Polynomial Fitting (MPF), Gaussian Processes Regression (GPR), and Least Squares Support Vector Machine (LSSVM).
  • Optimization of LSSVM using Chaos Estimation of Distributed Algorithm (CEDA) for enhanced parameter tuning.

Main Results:

  • LSSVM demonstrated superior performance over MPF and GPR in compensating for gyroscopic effects.
  • CEDA showed improved convergence speed and anti-premature ability compared to traditional distributed algorithms.
  • CEDA-LSSVM outperformed GA-LSSVM and PSO-LSSVM, achieving a torque calculation relative error of 10⁻⁴.

Conclusions:

  • The proposed CEDA-LSSVM method effectively reduces gyroscopic interference in SAW torque sensors.
  • This compensation technique significantly enhances sensor accuracy under high rotational speeds.
  • The findings provide a foundation for the engineering application of SAW torque sensors in demanding environments.