Torque ripple reduction in switched reluctance motor drives using B-spline neural networks

Zhengyu Lin; Donald S. Reay; Barry W. Williams; Xiangning He

doi:10.1109/IAS.2005.1518845

Torque ripple reduction in switched reluctance motor drives using B-spline neural networks

Zhengyu Lin, Donald S. Reay, Barry W. Williams, Xiangning He

School of Engineering & Physical Sciences

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

2 Citations (Scopus)

Abstract

A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented in this paper. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weights updating algorithm of the BSNN, the appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not required high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme. © 2005 IEEE.

Original language	English
Title of host publication	Conference Record of the 2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting
Pages	2726-2733
Number of pages	8
Volume	4
DOIs	https://doi.org/10.1109/IAS.2005.1518845
Publication status	Published - 2005
Event	2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting - Kowloon, Hong Kong, China Duration: 2 Oct 2005 → 6 Oct 2005

Conference

Conference	2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting
Country/Territory	China
City	Kowloon, Hong Kong
Period	2/10/05 → 6/10/05

Keywords

B-spline neural network
Switched reluctance motor
Torque ripple

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10.1109/IAS.2005.1518845

Cite this

@inproceedings{469525a5732740de80393e955fe5a91d,

title = "Torque ripple reduction in switched reluctance motor drives using B-spline neural networks",

abstract = "A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented in this paper. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weights updating algorithm of the BSNN, the appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not required high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme. {\textcopyright} 2005 IEEE.",

keywords = "B-spline neural network, Switched reluctance motor, Torque ripple",

author = "Zhengyu Lin and Reay, {Donald S.} and Williams, {Barry W.} and Xiangning He",

year = "2005",

doi = "10.1109/IAS.2005.1518845",

language = "English",

isbn = "0780392086",

volume = "4",

pages = "2726--2733",

booktitle = "Conference Record of the 2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting",

note = "2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting ; Conference date: 02-10-2005 Through 06-10-2005",

}

Lin, Z, Reay, DS, Williams, BW & He, X 2005, Torque ripple reduction in switched reluctance motor drives using B-spline neural networks. in Conference Record of the 2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting. vol. 4, pp. 2726-2733, 2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting, Kowloon, Hong Kong, China, 2/10/05. https://doi.org/10.1109/IAS.2005.1518845

TY - GEN

T1 - Torque ripple reduction in switched reluctance motor drives using B-spline neural networks

AU - Lin, Zhengyu

AU - Reay, Donald S.

AU - Williams, Barry W.

AU - He, Xiangning

PY - 2005

Y1 - 2005

N2 - A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented in this paper. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weights updating algorithm of the BSNN, the appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not required high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme. © 2005 IEEE.

AB - A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented in this paper. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weights updating algorithm of the BSNN, the appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not required high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme. © 2005 IEEE.

KW - B-spline neural network

KW - Switched reluctance motor

KW - Torque ripple

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U2 - 10.1109/IAS.2005.1518845

DO - 10.1109/IAS.2005.1518845

M3 - Conference contribution

SN - 0780392086

SN - 9780780392083

VL - 4

SP - 2726

EP - 2733

BT - Conference Record of the 2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting

T2 - 2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting

Y2 - 2 October 2005 through 6 October 2005

ER -

Torque ripple reduction in switched reluctance motor drives using B-spline neural networks

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