Calculation of BSRM´s inductance with PSO-BPNN

Author

Xiang Qianwen ; Zhang Xinhua ; Sun Yukun

Author_Institution

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

fYear

2011

fDate

22-24 July 2011

Firstpage

1424

Lastpage

1427

Abstract

Inductance characteristic has a great effect on bearingless switched reluctance motor (BSRM) control which is difficult to solve accurately. Particle swarm optimization (PSO) is used in back propagation neural network (BPNN) inductance model. When the BPNN is trained with sufficient samples, PSO is applied to optimize weights of BPNN. Building the PSO-BPNN model of inductance and evaluating performances of the proposed model by error compute. The results demonstrate that PSO-BPNN inductance models perform satisfactory forecast accuracy and convergent speed.

Keywords

backpropagation; inductance; machine control; neural nets; particle swarm optimisation; reluctance motors; BSRM; PSO-BPNN; Particle swarm optimization; back propagation neural network; bearingless switched reluctance motor control; inductance model; Force; Inductance; Magnetic levitation; Reluctance motors; Saturation magnetization; Switches; Windings; PSO-BPNN; back propagation neural network; bearingless switched reluctance motor; inductance characteristic; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2011 30th Chinese

Conference_Location

Yantai

ISSN

1934-1768

Print_ISBN

978-1-4577-0677-6

Electronic_ISBN

1934-1768

Type

conf

Filename

6000380