Abstract
This paper presents a novel control scheme for torque ripple reduction in switched reluctance motors (SRMs) operating at low constant speeds. The control scheme is implemented using a CMAC neural network, trained with a modified LMS algorithm. This algorithm uses a varying learning rate function (LRF) which is defined as a function of the rotor angle of the motor under control. Experimental measurements of the static torque production of a 4 kW, four-phase SRM form the basis of simulation studies of this approach. The simulation results demonstrate that a learned current profile, capable of minimizing torque ripple while having high power efficiency, can be obtained by selecting a LRF with suitable turn-on and turn-off angles during the training of the network.
Original language | English |
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Title of host publication | Proceedings of the 1997 American Control Conference. Part 3 (of 6) |
Pages | 237-241 |
Number of pages | 5 |
Volume | 1 |
Publication status | Published - 1997 |
Event | 1997 American Control Conference - Albuquerque, NM, USA Duration: 4 Jun 1997 → 6 Jun 1997 |
Conference
Conference | 1997 American Control Conference |
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City | Albuquerque, NM, USA |
Period | 4/06/97 → 6/06/97 |