On-line modelling of switched reluctance motor for high performance current control

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper considers the implementation of high performance control for switched reluctance motors (SRMs) and presents a novel approach to the accurate on-line modeling of an SRM. An adaptive B-spline neural network is used to learn the non-linear flux-linkage, torque, incremental inductance, and back EMF characteristics of an SRM. The training of the B-spline neural network is accomplished on-line and in real-time. The system does not require a priori knowledge of the machine's electromagnetic characteristics. The potential of the method is demonstrated in simulation and experimentally using a 550W 8/6 4-phase SRM. © 2004 Heriot-Watt University, UK.

Original languageEnglish
Pages (from-to)763-768
Number of pages6
JournalIEE Conference Publication
Publication statusPublished - 2004
EventSecond International Conference on Power Electronics, Machines and Drives, PEMD 2004 - Edinburgh, United Kingdom
Duration: 31 Mar 20042 Apr 2004


  • B-spline neural work
  • On-line modeling
  • Switched reluctance motor


Dive into the research topics of 'On-line modelling of switched reluctance motor for high performance current control'. Together they form a unique fingerprint.

Cite this