On-Line Instant Torque Estimation of Switched Reluctance Motor Using Adaptive B-spline Neural Network

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

doi:10.1109/IECON.2003.1280187

On-Line Instant Torque Estimation of Switched Reluctance Motor Using Adaptive B-spline Neural Network

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

3 Citations (Scopus)

Abstract

A novel on-line instant torque estimation scheme for a switched reluctance motor (SRM) is presented. In the proposed method, an adaptive B-spline neural network is used to learn the non-linear flux-linkage and torque characteristics of a SRM. Due to the local nature of its generalisation properties, the training of the B-spline neural network is accomplished on-line and in real-time, and the system does not require a priori knowledge of the machine's electromagnetic characteristics. The potential of the method is demonstrated successfully in simulation and experimentally using a 300W 12/8 3-phase SRM.

Original language	English
Title of host publication	Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society, 2003
Pages	1033-1037
Number of pages	5
Volume	2
DOIs	https://doi.org/10.1109/IECON.2003.1280187
Publication status	Published - 2003
Event	29th Annual Conference of the IEEE Industrial Electronics Society - Roanoke, VA, United States Duration: 2 Nov 2003 → 6 Nov 2003

Conference

Conference	29th Annual Conference of the IEEE Industrial Electronics Society
Country/Territory	United States
City	Roanoke, VA
Period	2/11/03 → 6/11/03

Keywords

Neural network
Switched reluctance motors
Torque estimation

Access to Document

10.1109/IECON.2003.1280187

Cite this

@inproceedings{1cebc0371d444637ada171afbbc72ce9,

title = "On-Line Instant Torque Estimation of Switched Reluctance Motor Using Adaptive B-spline Neural Network",

abstract = "A novel on-line instant torque estimation scheme for a switched reluctance motor (SRM) is presented. In the proposed method, an adaptive B-spline neural network is used to learn the non-linear flux-linkage and torque characteristics of a SRM. Due to the local nature of its generalisation properties, the training of the B-spline neural network is accomplished on-line and in real-time, and the system does not require a priori knowledge of the machine's electromagnetic characteristics. The potential of the method is demonstrated successfully in simulation and experimentally using a 300W 12/8 3-phase SRM.",

keywords = "Neural network, Switched reluctance motors, Torque estimation",

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

note = "INSPEC Accession Number: 8031637; 29th Annual Conference of the IEEE Industrial Electronics Society ; Conference date: 02-11-2003 Through 06-11-2003",

year = "2003",

doi = "10.1109/IECON.2003.1280187",

language = "English",

isbn = "0-7803-7906-3",

volume = "2",

pages = "1033--1037",

booktitle = "Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society, 2003",

}

Lin, Z, Reay, DS, Williams, BW & He, XN 2003, On-Line Instant Torque Estimation of Switched Reluctance Motor Using Adaptive B-spline Neural Network. in Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society, 2003. vol. 2, pp. 1033-1037, 29th Annual Conference of the IEEE Industrial Electronics Society, Roanoke, VA, United States, 2/11/03. https://doi.org/10.1109/IECON.2003.1280187

On-Line Instant Torque Estimation of Switched Reluctance Motor Using Adaptive B-spline Neural Network. / Lin, Zhengyu; Reay, Donald Shewan; Williams, Barry Wayne et al.
Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society, 2003. Vol. 2 2003. p. 1033-1037.

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

TY - GEN

T1 - On-Line Instant Torque Estimation of Switched Reluctance Motor Using Adaptive B-spline Neural Network

AU - Lin, Zhengyu

AU - Reay, Donald Shewan

AU - Williams, Barry Wayne

AU - He, Xiang Ning

N1 - INSPEC Accession Number: 8031637

PY - 2003

Y1 - 2003

N2 - A novel on-line instant torque estimation scheme for a switched reluctance motor (SRM) is presented. In the proposed method, an adaptive B-spline neural network is used to learn the non-linear flux-linkage and torque characteristics of a SRM. Due to the local nature of its generalisation properties, the training of the B-spline neural network is accomplished on-line and in real-time, and the system does not require a priori knowledge of the machine's electromagnetic characteristics. The potential of the method is demonstrated successfully in simulation and experimentally using a 300W 12/8 3-phase SRM.

AB - A novel on-line instant torque estimation scheme for a switched reluctance motor (SRM) is presented. In the proposed method, an adaptive B-spline neural network is used to learn the non-linear flux-linkage and torque characteristics of a SRM. Due to the local nature of its generalisation properties, the training of the B-spline neural network is accomplished on-line and in real-time, and the system does not require a priori knowledge of the machine's electromagnetic characteristics. The potential of the method is demonstrated successfully in simulation and experimentally using a 300W 12/8 3-phase SRM.

KW - Neural network

KW - Switched reluctance motors

KW - Torque estimation

UR - http://www.scopus.com/inward/record.url?scp=1442353554&partnerID=8YFLogxK

U2 - 10.1109/IECON.2003.1280187

DO - 10.1109/IECON.2003.1280187

M3 - Conference contribution

SN - 0-7803-7906-3

VL - 2

SP - 1033

EP - 1037

BT - Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society, 2003

T2 - 29th Annual Conference of the IEEE Industrial Electronics Society

Y2 - 2 November 2003 through 6 November 2003

ER -

On-Line Instant Torque Estimation of Switched Reluctance Motor Using Adaptive B-spline Neural Network

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this