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
相关论文
共 50 条
  • [31] Torque control of switched reluctance motors based on flexible neural network
    Ge, BM
    de Almeida, AT
    Ferreira, FJTE
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS, 2005, 3498 : 173 - 178
  • [32] Four-Quadrant Position Sensor less Control in Switched Reluctance Motor Drives Based on Sliding Mode Observer
    Tan, Guojun
    Ma, Zhenglan
    Kuai, Songyan
    Zhang, Xulong
    [J]. 2009 INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS, VOLS 1-3, 2009, : 958 - +
  • [33] Novel Hybrid Recurrent Hermite Neural Network Control for Switched Reluctance Motor Drive
    Lin, Chih-Hong
    Shih, Yen-Chen
    [J]. PROCEEDINGS OF THE 2018 13TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2018), 2018, : 1022 - 1027
  • [34] Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor
    Ge, Baoming
    de Almeida, Anibal T.
    [J]. INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE, 2011, 5 (03) : 86 - 98
  • [35] Neural Networks Based Nonlinear H∞ Control for Linear Switched Reluctance Motor
    Li, Huiyan
    Liu, Yuliang
    Wang, Jiang
    Wong, Y. K.
    Chan, W. L.
    [J]. ASCC: 2009 7TH ASIAN CONTROL CONFERENCE, VOLS 1-3, 2009, : 796 - 801
  • [36] The Design of BP Neural Network Modeling for Switched Reluctance Motor
    Qiao, Dong-kai
    Cai, Mei-qing
    Li, Guo-le
    [J]. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONIC ENGINEERING (EEE 2019), 2019, 185 : 164 - 168
  • [37] Sensor-less Direct Torque Control using Inductance Peak Detection for Switched Reluctance Motor
    Kim, Sangyub
    Kim, Jae-Hoon
    Kim, Rae-Young
    [J]. 2015 IEEE CONFERENCE ON ENERGY CONVERSION (CENCON), 2015, : 7 - 12
  • [38] Neural Network Based Torque Control of Switched Reluctance Motor for Hybrid Electric Vehicle Propulsion at Low Speeds
    Lu, Dongyun
    Kar, Narayan C.
    [J]. 2009 IEEE INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY, 2009, : 415 - 420
  • [39] Simulation of neural networks to sensorless control of switched reluctance motor
    Ooi, HS
    Green, TC
    [J]. SEVENTH INTERNATIONAL CONFERENCE ON POWER ELECTRONICS AND VARIABLE SPEED DRIVES, 1998, (456): : 281 - 286
  • [40] Nonlinear modeling of switched reluctance motor based on combination of neural network and genetic algorithm
    Wang, XL
    Zhang, YH
    Liu, HJ
    Huang, H
    [J]. ICEMS 2005: Proceedings of the Eighth International Conference on Electrical Machines and Systems, Vols 1-3, 2005, : 572 - 574
12345