Position calculation of switched reluctance motors based on genetic algorithm-fuzzy logic method

Jiang Wang, Shuang Xia Niu, Xiang Yang Fei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Intelligent approaches were applied to the procedure of nonlinear modeling for switched reluctance motor (SRM) and exact estimation of the rotor position. From the measured data of stator current, winding inductance and rotor angle position in experiment, the nonlinear characteristic of SRM was obtained and fuzzy rules were further developed. Learning ability of neural network and global optimization of genetic algorithm (GA) was employed to minimize the modeling error. The proposed algorithm can realize exact estimation of the rotor position with certain robustness and reliability and can directly replace the sensor of position. Complexity can be avoided by intelligent modeling of nonlinear system. The results of simulation demonstrate efficiency of the modeling.

Original languageEnglish
Pages (from-to)722-729
Number of pages8
JournalTianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology
Volume38
Issue number8
Publication statusPublished - Aug 2005
Externally publishedYes

Keywords

  • Fuzzy logic
  • Genetic algorithm
  • Neuro-networks
  • Switched reluctance motor (SRM)

ASJC Scopus subject areas

  • General

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