Modeling inductance for bearingless switched reluctance motor based on PSO-LSSVM

Author

Xiang, Qianwen ; Sun, Yukun ; Ji, Xiaofu

Author_Institution

Coll. of Electron. & Inf. Eng., Jiangsu Univ., Zhenjiang, China

fYear

2011

fDate

23-25 May 2011

Firstpage

800

Lastpage

803

Abstract

The least square support vector machine (LS-SVM) inductance model optimized by the particle swarm optimization (PSO) algorithm is presented for bearingless switched reluctance motor (BSRM). The training sample is first obtained using the 3D finite element model (FEM) of the prototype, and then LS-SVM model is built, whose hyper-parameters are optimized using PSO algorithm. The absolute error and relative error are computed, which demonstrate the high accuracy of the proposed model.

Keywords

finite element analysis; inductance; least squares approximations; particle swarm optimisation; reluctance motors; support vector machines; 3D finite element model; LSSVM; PSO; PSO algorithm; absolute error; bearingless switched reluctance motor; hyper parameters; inductance model; least square support vector machine; particle swarm optimization; relative error; Computational modeling; Data models; Inductance; Magnetic levitation; Reluctance motors; Support vector machines; Switches; Bearingless switched reluctance motor; Inductance characteristic; Least square support vector machine; PSO-LSSVM; Particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2011 Chinese

Conference_Location

Mianyang

Print_ISBN

978-1-4244-8737-0

Type

conf

DOI

10.1109/CCDC.2011.5968291

Filename

5968291