Sensorless Driving Method of Permanent-Magnet Synchronous Motors Based on Neural Networks

Hai Jiao Guo, S. Sagawa, T. Watanabe, Osamu Ichinokura

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

In sensorless driving of permanent-magnet synchronous motors, it is difficult to deal with the nonlinear characteristic of the magnetic circuit and the harmonics of magnetic density distribution in the air gap. Using a neural network has been considered to be a powerful tool but, unfortunately, only few simulation-based works can be found. In this paper, an experiment system and successful experiment results using our proposed sensorless driving method of permanent-magnet synchronous motors based on neural networks will be presented. The method estimates the position errors from electromotive force instead of directly estimating the position using neural networks and then using the approximate algorithm to obtain the rotor position. Experiment results show that the method has excellent possibility in practical applications.

Original languageEnglish
Pages (from-to)3247-3249
Number of pages3
JournalIEEE Transactions on Magnetics
Volume39
Issue number5 II
DOIs
Publication statusPublished - 2003 Sep 1

Keywords

  • Approximate algorithm
  • Magnet synchronous motors
  • Neural networks
  • Position-sensorless driving

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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