Artificial neural network based torque calculation of switched reluctance motor without locking the rotor

Fuat Kucuk, Hiroki Goto, Hai Jiao Guo, Osamu Ichinokura

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Feedback of motor torque is required in most of switched reluctance (SR) motor applications in order to control torque and its ripple. An SR motor shows highly nonlinear property which does not allow calculating torque analytically. Torque can be directly measured by torque sensor, but it inevitably increases the cost and has to be properly mounted on the motor shaft. Instead of torque sensor, finite element analysis (FEA) may be employed for torque calculation. However, motor modeling and calculation takes relatively long time. The results of FEA may also differ from the actual results. The most convenient way seems to calculate torque from the measured values of rotor position, current, and flux linkage while locking the rotor at definite positions. However, this method needs an extra assembly to lock the rotor. In this study, a novel torque calculation based on artificial neural networks (ANNs) is presented. Magnetizing data are collected while a 6/4 SR motor is running. They need to be interpolated for torque calculation. ANN is very strong tool for data interpolation. ANN based torque estimation is verified on the 6/4 SR motor and is compared by FEA based torque estimation to show its validity.

Original languageEnglish
Article number07F103
JournalJournal of Applied Physics
Issue number7
Publication statusPublished - 2009 Apr 27

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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