Position estimation of outer rotor PMSM using linear hall effect sensors and neural networks

Yuyao Wang, Yovahn Hoole, Kiruba Haran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A position estimator for an outer rotor permanent magnet synchronous machine (PMSM) is presented and evaluated. This proposed estimator uses a machine-learning based neural network algorithm to interpret the signals, which are obtained from linear Hall-effect sensors located in the fringe field of the rotor. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods. Learning signal data sets are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed.

Original languageEnglish (US)
Title of host publication2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages895-900
Number of pages6
ISBN (Electronic)9781538693490
DOIs
StatePublished - May 2019
Event11th IEEE International Electric Machines and Drives Conference, IEMDC 2019 - San Diego, United States
Duration: May 12 2019May 15 2019

Publication series

Name2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019

Conference

Conference11th IEEE International Electric Machines and Drives Conference, IEMDC 2019
CountryUnited States
CitySan Diego
Period5/12/195/15/19

Keywords

  • Hall effect sensor
  • Neural network
  • Permanent magnet synchronous machine
  • Pmsm
  • Position estimation

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering

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