A RBF Neural Network Based Sensor less Control Scheme for Switched Reluctance Motor

被引：0
|
作者：
Cai, Jun [1 ]
Deng, Zhiquan [1 ,2 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing, Jiangsu, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Dept Elect Engn, Nanjing, Jiangsu, Peoples R China
来源：
INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE | 2012年 / 7卷 / 06期
关键词：
Switched Reluctance Motor; Sensorless; RBF Neural Network; Flux Linkage Thresholds; POSITION ESTIMATION; ROTOR POSITION; DRIVE;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A new sensorless control method for Switched Reluctance Motor (SRM) is proposed in this paper. To simplify the measuring procedure and enhance the accuracy of the flux linkage, anew flux linkage calculation method is developed by combing the three-dimensional Finite element method and the measurement of the aligned flux-linkage. The Radial Basis Function (RBF) neural network is. utilized for modeling the nonlinear flux linkage characteristics, and a 5-order polynomial fitting model is developed for modeling the flux/current characteristics at the turn-OFF angle. Thus, using the Turn-on angle, Turn-OFF angle and the phase current, the flux-linkage thresholds at the Turn-ON angle of the next driving phase and the Turn-OFF angle of the current driving phase can be estimated. By comparing the estimated flux linkage with the Turn-ON and Turn-OFF flux linkage thresholds, the driving signals of each phase can be estimated, which can be used for rotor speed estimation and sensorless control. Experiments verify, the validity of the sensorless scheme. Copyright (C) 2012 Praise Worthy Prize S.r.L - All rights reserved.
引用
收藏
页码：6026 / 6034
页数：9
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