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Nonlinearity analysis and parameters optimization for an inductive angle sensor.

Lin Ye1, Ming Yang2, Liang Xu3

  • 1Department of Instrument Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China. linye@sjtu.edu.cn.

Sensors (Basel, Switzerland)
|March 5, 2014
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Summary
This summary is machine-generated.

This study optimized an inductive angle sensor using finite element method (FEM) and particle swarm optimization (PSO). The optimized sensor design significantly reduced nonlinearity errors, achieving high accuracy for angle measurement.

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Area of Science:

  • Sensor Technology
  • Electromagnetics
  • Optimization Algorithms

Background:

  • Inductive angle sensors are crucial for precise angular measurement.
  • Sensor complexity often leads to nonlinearity errors, impacting performance.
  • Optimizing structural parameters is key to minimizing these errors.

Purpose of the Study:

  • To propose a nonlinearity analysis and design method for inductive angle sensors.
  • To investigate the influence of structural parameters on nonlinearity errors.
  • To achieve minimal nonlinearity error through optimized sensor design.

Main Methods:

  • Utilizing the finite element method (FEM) for simulation and analysis.
  • Employing particle swarm optimization (PSO) for parameter optimization.
  • Combining FEM and PSO to iteratively refine sensor design.

Main Results:

  • Simulation showed a nonlinearity error of 0.053% within a -60° to 60° range.
  • Experimental validation of a prototype sensor yielded a nonlinearity error of 0.081%.
  • The optimized design demonstrated a significant reduction in nonlinearity.

Conclusions:

  • The proposed FEM and PSO approach effectively minimizes nonlinearity errors in inductive angle sensors.
  • Optimized sensor design ensures high accuracy and reliability in angle measurement.
  • This method provides a robust framework for developing advanced inductive angle sensors.