2010Unpublished venueRequires access

Nonlinear modeling for switched reluctance motor by measuring flux linkage curves

Yan Cai, Qingxin Yang, Li‐Hua Su, Yanbin Wen, Yiming You

Open publisher page 10 citations

Abstract

Two kinds of modeling methods are studied based on measuring flux linkage curves for switched reluctance motor (SRM) which the machine geometry is unknowable. A simplified model of SRM is presented for general switched reluctance drive (SRD), which is easy to be built and its accuracy has been improved compared with the quasi-linear model. To get an accurate model, the BP Neural Network (BPNN) nonlinear model of SRM is developed based on Levenberg-Marquardt algorithm by measuring flux linkage characteristics. Compared with measured data of flux linkage and torque, the BPNN model of SRM is proved accurate and meet high performance SRD. Both kinds of the modeling methods are suitable for SRD with different control performance requirement respectively.

About this research paper

What this paper is about

Two kinds of modeling methods are studied based on measuring flux linkage curves for switched reluctance motor (SRM) which the machine geometry is unknowable. A simplified model of SRM is presented for general switched reluctance drive (SRD), which is easy to be built and its accuracy has been improved compared with the quasi-linear model. To get an accurate model, the BP Neural Network (BPNN) nonlinear model of SRM is developed based on Levenberg-Marquardt algorithm by measuring flux linkage characteristics. Compared with measured data of flux linkage and torque, the BPNN model of SRM is proved accurate and meet high performance SRD. Both kinds of the modeling methods are suitable for SRD with different control performance requirement respectively.

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OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Two kinds of modeling methods are studied based on measuring flux linkage curves for switched reluctance motor (SRM) which the machine geometry is unknowable. A simplified model of SRM is presented for general switched reluctance drive (SRD), which is easy to be built and its accuracy has been improved compared with the quasi-linear model. To get an accurate model, the BP Neural Network (BPNN) nonlinear model of SRM is developed based on Levenberg-Marquardt algorithm by measuring flux linkage characteristics. Compared with measured data of flux linkage and torque, the BPNN model of SRM is proved accurate and meet high performance SRD. Both kinds of the modeling methods are suitable for SRD with different control performance requirement respectively.

Key concepts: Switched reluctance motor, Flux linkage, Control theory (sociology), Torque, Linkage (software), Nonlinear system, Artificial neural network, Computer science

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