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Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor

Using the finite element method (FEM) and particle swarm optimization (PSO), a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearit...

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Autores principales: Ye, Lin, Yang, Ming, Xu, Liang, Zhuang, Xiaoqi, Dong, Zhaopeng, Li, Shiyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4003933/
https://www.ncbi.nlm.nih.gov/pubmed/24590353
http://dx.doi.org/10.3390/s140304111
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author Ye, Lin
Yang, Ming
Xu, Liang
Zhuang, Xiaoqi
Dong, Zhaopeng
Li, Shiyang
author_facet Ye, Lin
Yang, Ming
Xu, Liang
Zhuang, Xiaoqi
Dong, Zhaopeng
Li, Shiyang
author_sort Ye, Lin
collection PubMed
description Using the finite element method (FEM) and particle swarm optimization (PSO), a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearity errors is a critical step in designing an effective sensor. Key parameters are selected for the design based on the parameters' effects on the nonlinearity errors. The finite element method and particle swarm optimization are combined for the sensor design to get the minimal nonlinearity error. In the simulation, the nonlinearity error of the optimized sensor is 0.053% in the angle range from −60° to 60°. A prototype sensor is manufactured and measured experimentally, and the experimental nonlinearity error is 0.081% in the angle range from −60° to 60°.
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spelling pubmed-40039332014-04-29 Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor Ye, Lin Yang, Ming Xu, Liang Zhuang, Xiaoqi Dong, Zhaopeng Li, Shiyang Sensors (Basel) Article Using the finite element method (FEM) and particle swarm optimization (PSO), a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearity errors is a critical step in designing an effective sensor. Key parameters are selected for the design based on the parameters' effects on the nonlinearity errors. The finite element method and particle swarm optimization are combined for the sensor design to get the minimal nonlinearity error. In the simulation, the nonlinearity error of the optimized sensor is 0.053% in the angle range from −60° to 60°. A prototype sensor is manufactured and measured experimentally, and the experimental nonlinearity error is 0.081% in the angle range from −60° to 60°. MDPI 2014-02-28 /pmc/articles/PMC4003933/ /pubmed/24590353 http://dx.doi.org/10.3390/s140304111 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Ye, Lin
Yang, Ming
Xu, Liang
Zhuang, Xiaoqi
Dong, Zhaopeng
Li, Shiyang
Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
title Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
title_full Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
title_fullStr Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
title_full_unstemmed Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
title_short Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
title_sort nonlinearity analysis and parameters optimization for an inductive angle sensor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4003933/
https://www.ncbi.nlm.nih.gov/pubmed/24590353
http://dx.doi.org/10.3390/s140304111
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