Use of neural networks to enhance sensorless position detection in switched reluctance motors

D. S. Reay, Y. Dessouky, B. W. Williams

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

4 Citations (Scopus)

Abstract

This paper describes a novel method of sensorless position detection for a switched reluctance motor (SRM). The approach requires no special converter or sensor circuitry, and does not rely on accurate prior knowledge of the magnetic characteristics of the motor. The technique is based on the use of the main converters to inject short, fixed duration, diagnostic current pulses simultaneously into two unenergised phases of a four-phase SRM. Previously, such a technique has been used to estimate the inductance of the motor phase windings and, using stored knowledge of the relationship between inductance L, rotor position ?, and current i, to estimate rotor position. The approach described in this paper is novel in two respects. Firstly, it does not rely on prior knowledge of the function L(?) but merely makes the assumption that L varies substantially as sin(Nr?), where Nr is the number of rotor poles. Secondly, the approach learns from good estimates of position and, once it has done this, is able to use this knowledge where performance of the estimation algorithm degrades (principally at low speeds of rotation).

Original languageEnglish
Title of host publicationProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 2 (of 5)
Pages1774-1778
Number of pages5
Volume2
Publication statusPublished - 1998
Event1998 IEEE International Conference on Systems, Man, and Cybernetics - San Diego, CA, United States
Duration: 11 Oct 199814 Oct 1998

Conference

Conference1998 IEEE International Conference on Systems, Man, and Cybernetics
CountryUnited States
CitySan Diego, CA
Period11/10/9814/10/98

Fingerprint Dive into the research topics of 'Use of neural networks to enhance sensorless position detection in switched reluctance motors'. Together they form a unique fingerprint.

Cite this