A diagonally recurrent neural network approach to sensorless operation of the permanent magnet synchronous motor

Todd D. Batzel, Kwang Y. Lee

Research output: Contribution to conferencePaper

5 Scopus citations

Abstract

Rotor position sensorless control of the permanent magnet synchronous motor (PMSM) typically requires knowledge of the PMSM structure and parameters, which in some situations are not readily available or may be difficult to obtain. Due to this limitation, an alternative approach to rotor position sensorless control of the PMSM using a diagonally recurrent neural network (DRNN) is considered. The DRNN is a dynamic mapping, requires fewer neurons, and converges quickly compared to feedforward and fully recurrent neural networks. Experimental results of the proposed neural observer to PMSM rotor estimation are presented.

Original languageEnglish (US)
Pages2441-2445
Number of pages5
StatePublished - 2000
EventProceedings of the 2000 Power Engineering Society Summer Meeting - Seattle, WA, United States
Duration: Jul 16 2000Jul 20 2000

Other

OtherProceedings of the 2000 Power Engineering Society Summer Meeting
CountryUnited States
CitySeattle, WA
Period7/16/007/20/00

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Batzel, T. D., & Lee, K. Y. (2000). A diagonally recurrent neural network approach to sensorless operation of the permanent magnet synchronous motor. 2441-2445. Paper presented at Proceedings of the 2000 Power Engineering Society Summer Meeting, Seattle, WA, United States.