Switched reluctance motor control via fuzzy adaptive systems

Donald S. Reay, Mehran Mirkazemi-Moud, Tim C. Green, Barry W. Williams

Research output: Contribution to journalArticle

Abstract

This article presents the application of fuzzy adaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzy adaptive systems is then described. Experimental measurements of the static torque production characteristics of a 4 kW, four-phase switched reluctance motor form the basis of simulation studies of this novel approach. The simulation results demonstrate the capability of fuzzy adaptive systems to learn non-linear current profiles that minimize torque ripple. The use of fuzzy systems in this application has potential advantages where the incorporation of a priori information, expressed linguistically, is concerned. Experimental results illustrate the effectiveness of the approach.

Original languageEnglish
Pages (from-to)8-15
Number of pages8
JournalIEEE Control Systems Magazine
Volume15
Issue number3
DOIs
Publication statusPublished - Jun 1995

Fingerprint

Reluctance motors
Adaptive systems
Torque
Fuzzy systems
Linearization
Neural networks

Cite this

Reay, Donald S. ; Mirkazemi-Moud, Mehran ; Green, Tim C. ; Williams, Barry W. / Switched reluctance motor control via fuzzy adaptive systems. In: IEEE Control Systems Magazine. 1995 ; Vol. 15, No. 3. pp. 8-15.
@article{b918f49dd95a4cb384543bf05c4200e6,
title = "Switched reluctance motor control via fuzzy adaptive systems",
abstract = "This article presents the application of fuzzy adaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzy adaptive systems is then described. Experimental measurements of the static torque production characteristics of a 4 kW, four-phase switched reluctance motor form the basis of simulation studies of this novel approach. The simulation results demonstrate the capability of fuzzy adaptive systems to learn non-linear current profiles that minimize torque ripple. The use of fuzzy systems in this application has potential advantages where the incorporation of a priori information, expressed linguistically, is concerned. Experimental results illustrate the effectiveness of the approach.",
author = "Reay, {Donald S.} and Mehran Mirkazemi-Moud and Green, {Tim C.} and Williams, {Barry W.}",
year = "1995",
month = "6",
doi = "10.1109/37.387611",
language = "English",
volume = "15",
pages = "8--15",
journal = "IEEE Control Systems Magazine",
issn = "0272-1708",
number = "3",

}

Switched reluctance motor control via fuzzy adaptive systems. / Reay, Donald S.; Mirkazemi-Moud, Mehran; Green, Tim C.; Williams, Barry W.

In: IEEE Control Systems Magazine, Vol. 15, No. 3, 06.1995, p. 8-15.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Switched reluctance motor control via fuzzy adaptive systems

AU - Reay, Donald S.

AU - Mirkazemi-Moud, Mehran

AU - Green, Tim C.

AU - Williams, Barry W.

PY - 1995/6

Y1 - 1995/6

N2 - This article presents the application of fuzzy adaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzy adaptive systems is then described. Experimental measurements of the static torque production characteristics of a 4 kW, four-phase switched reluctance motor form the basis of simulation studies of this novel approach. The simulation results demonstrate the capability of fuzzy adaptive systems to learn non-linear current profiles that minimize torque ripple. The use of fuzzy systems in this application has potential advantages where the incorporation of a priori information, expressed linguistically, is concerned. Experimental results illustrate the effectiveness of the approach.

AB - This article presents the application of fuzzy adaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzy adaptive systems is then described. Experimental measurements of the static torque production characteristics of a 4 kW, four-phase switched reluctance motor form the basis of simulation studies of this novel approach. The simulation results demonstrate the capability of fuzzy adaptive systems to learn non-linear current profiles that minimize torque ripple. The use of fuzzy systems in this application has potential advantages where the incorporation of a priori information, expressed linguistically, is concerned. Experimental results illustrate the effectiveness of the approach.

U2 - 10.1109/37.387611

DO - 10.1109/37.387611

M3 - Article

VL - 15

SP - 8

EP - 15

JO - IEEE Control Systems Magazine

JF - IEEE Control Systems Magazine

SN - 0272-1708

IS - 3

ER -