Type-V exponential regression for online sensorless position estimation of switched reluctance motor

Yan Tai Chang, K. W.Eric Cheng, Siu Lau Ho

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

The idea of sensorless position sensing of switched reluctance motor (SRM) is attractive to researchers because of the increased reliability, robustness, and cost reduction compared to conventional drives. Sensorless drive is particularly useful in electric transportation applications where the environment is too hostile for physical position sensors, such as inside an electric car or bus. This paper presents a new method to estimate the motor positions during startup or at flying restart. Unlike most of the methods described in the literature, the algorithm, based only on the general magnetic characteristics of an SRM, can provide exact rotor positions without specific motor magnetic information. The calculation is simple and can be implemented easily and efficiently with a microcontroller by users in industry.
Original languageEnglish
Article number6882824
Pages (from-to)1351-1359
Number of pages9
JournalIEEE/ASME Transactions on Mechatronics
Volume20
Issue number3
DOIs
Publication statusPublished - 1 Jun 2015

Keywords

  • Exponential regression
  • Position estimation
  • Sensorless
  • Startup
  • Switched reluctance motor (SRM)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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