• CN：11-2187/TH
• ISSN：0577-6686

›› 2007, Vol. 43 ›› Issue (2): 151-155.

• 论文 •

### q<1永磁直线同步电动机齿槽力波动的径向基神经网络预估器

1. 浙江大学应用物理研究所;浙江大学现代制造工程研究所
• 发布日期:2007-02-15

### RADICAL BASIS FUNCTIONAL NETWORK-BASED COGGING FORCE ESTIMATOR OF PERMANENT MAGNETIC LINER SYNCHRONOUS MOTOR WITH q<1 STRUCTURE

SHAO Bo;CAO Zhitong;XU Yuetong

1. Institute of Applied Physics, Zhejiang University Institute of Advanced Manufacturing Engineering, Zhejiang University
• Published:2007-02-15

Abstract: The cogging force is of great impact to the efficiency of permanent magnetic liner synchronous motor(PMLSM) es-pecially in high precision and low speed. According to the frac-tional slot with q<1 structure of PMLSM, FEM is used to ana-lyze the influence of cogging force. Supposed estimator based on radical basis functional network(RBFN) is presented by improved algorithm. To select the right spread factor of base function, the accelerate fuzzy C-means(AFCM) is used in data clustering. Then, OLSA is used to choose the center vector from the clustering center. Comparing to the estimator based on back propagation neural network(BPNN) with momentum method, the novel estimator increases the clustering of neural network with boosting learning rate. Results show the fractional slot with q <1 structure effectively reduces the influence of cogging force in PMLSM. Through the estimator based on RBFN, the parameters of the PMLSM can be evaluated in the design pe-riod. By satisfying the standards of cogging force ripple, the estimator achieves the agility demand and improves the design level of PMLSM.