On-line torque estimation in a switched reluctance motor for torque ripple minimisation

Zhengyu Lin; Donald Shewan Reay; Barry Wayne Williams; Xiang Ning He

doi:10.1109/ISIE.2004.1571947

On-line torque estimation in a switched reluctance motor for torque ripple minimisation

Zhengyu Lin, Donald Shewan Reay, Barry Wayne Williams, Xiang Ning He

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

This paper considers torque ripple minimisation control for switched reluctance motors (SRMs) and presents a novel on-line approach to the estimation of instantaneous torque. An adaptive B-spline neural network is used to learn the nonlinear flux linkage and torque characteristics of an SRM. The training of the B-spline neural network is accomplished on-line in real-time. anid the system does not require a priori know-ledge of the SRM"s electromagnetic characteristics. The potential of the torque estimation method is demonstrated in simulation and experimentallx using a 550W 8/6 four-phase SRM operating in saturation, and it has been applied successfully to torque ripple minimisation. © 2004 IEEE.

Original language	English
Title of host publication	2004 IEEE International Symposium on Industrial Electronics
Pages	981-985
Number of pages	5
Volume	2
DOIs	https://doi.org/10.1109/ISIE.2004.1571947
Publication status	Published - 2004
Event	2004 IEEE International Symposium on Industrial Electronics - Duration: 4 May 2004 → 7 May 2004

Conference

Conference	2004 IEEE International Symposium on Industrial Electronics
Abbreviated title	IEEE-ISlE
Period	4/05/04 → 7/05/04

Keywords

Modeling
SRM
Torque estimation

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10.1109/ISIE.2004.1571947

Cite this

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title = "On-line torque estimation in a switched reluctance motor for torque ripple minimisation",

abstract = "This paper considers torque ripple minimisation control for switched reluctance motors (SRMs) and presents a novel on-line approach to the estimation of instantaneous torque. An adaptive B-spline neural network is used to learn the nonlinear flux linkage and torque characteristics of an SRM. The training of the B-spline neural network is accomplished on-line in real-time. anid the system does not require a priori know-ledge of the SRM{"}s electromagnetic characteristics. The potential of the torque estimation method is demonstrated in simulation and experimentallx using a 550W 8/6 four-phase SRM operating in saturation, and it has been applied successfully to torque ripple minimisation. {\textcopyright} 2004 IEEE.",

keywords = "Modeling, SRM, Torque estimation",

author = "Zhengyu Lin and Reay, {Donald Shewan} and Williams, {Barry Wayne} and He, {Xiang Ning}",

note = "INSPEC Accession Number: 8748328; 2004 IEEE International Symposium on Industrial Electronics, IEEE-ISlE ; Conference date: 04-05-2004 Through 07-05-2004",

year = "2004",

doi = "10.1109/ISIE.2004.1571947",

language = "English",

isbn = "0-7803-8304-4",

volume = "2",

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booktitle = "2004 IEEE International Symposium on Industrial Electronics",

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T1 - On-line torque estimation in a switched reluctance motor for torque ripple minimisation

AU - Lin, Zhengyu

AU - Reay, Donald Shewan

AU - Williams, Barry Wayne

AU - He, Xiang Ning

N1 - INSPEC Accession Number: 8748328

PY - 2004

Y1 - 2004

N2 - This paper considers torque ripple minimisation control for switched reluctance motors (SRMs) and presents a novel on-line approach to the estimation of instantaneous torque. An adaptive B-spline neural network is used to learn the nonlinear flux linkage and torque characteristics of an SRM. The training of the B-spline neural network is accomplished on-line in real-time. anid the system does not require a priori know-ledge of the SRM"s electromagnetic characteristics. The potential of the torque estimation method is demonstrated in simulation and experimentallx using a 550W 8/6 four-phase SRM operating in saturation, and it has been applied successfully to torque ripple minimisation. © 2004 IEEE.

AB - This paper considers torque ripple minimisation control for switched reluctance motors (SRMs) and presents a novel on-line approach to the estimation of instantaneous torque. An adaptive B-spline neural network is used to learn the nonlinear flux linkage and torque characteristics of an SRM. The training of the B-spline neural network is accomplished on-line in real-time. anid the system does not require a priori know-ledge of the SRM"s electromagnetic characteristics. The potential of the torque estimation method is demonstrated in simulation and experimentallx using a 550W 8/6 four-phase SRM operating in saturation, and it has been applied successfully to torque ripple minimisation. © 2004 IEEE.

KW - Modeling

KW - SRM

KW - Torque estimation

UR - http://www.scopus.com/inward/record.url?scp=33745950770&partnerID=8YFLogxK

U2 - 10.1109/ISIE.2004.1571947

DO - 10.1109/ISIE.2004.1571947

M3 - Conference contribution

SN - 0-7803-8304-4

VL - 2

SP - 981

EP - 985

BT - 2004 IEEE International Symposium on Industrial Electronics

T2 - 2004 IEEE International Symposium on Industrial Electronics

Y2 - 4 May 2004 through 7 May 2004

ER -

On-line torque estimation in a switched reluctance motor for torque ripple minimisation

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Keywords

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