Please use this identifier to cite or link to this item:
Title: Low torque ripple control of switched reluctance motors using iterative learning
Authors: Sahoo, N.C.
Xu, J.X. 
Panda, S.K. 
Keywords: Current waveforms
Iterative learning control
Switched reluctance motor
Torque ripple
Issue Date: Dec-2001
Citation: Sahoo, N.C., Xu, J.X., Panda, S.K. (2001-12). Low torque ripple control of switched reluctance motors using iterative learning. IEEE Transactions on Energy Conversion 16 (4) : 318-326. ScholarBank@NUS Repository.
Abstract: Torque control of the switched reluctance motor is complicated by its highly nonlinear torque-current-position characteristics. The purpose of this paper is the development of simple and efficient control algorithms for the constant torque control of switched reluctance motors. The approach consists of two distinct steps, i.e., determination of appropriate phase current waveforms for some specified torque and the subsequent generation of suitable phase voltage profiles for faithful tracking of these waveforms by the respective stator windings of the motor. At both the stages of the control design, the principles of iterative learning control have been exploited. Firstly, the desired current waveform is generated by repeated corrections from iteration to iteration starting from the conventional rectangular current profile as the initial waveform. This scheme requires much less a priori knowledge of the magnetic characteristics of the motor. In the second stage, the voltage profiles to be impressed upon the stator phases for the tracking of the desired current waveforms are learnt iteratively. Simulation results show impressive response characteristics for a four-phase switched reluctance motor.
Source Title: IEEE Transactions on Energy Conversion
ISSN: 08858969
DOI: 10.1109/60.969470
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Dec 12, 2018


checked on Dec 12, 2018

Page view(s)

checked on Nov 17, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.