Title :
Torque ripple reduction in switched reluctance motor drives using B-spline neural networks
Author :
Lin, Zhiyun ; Reay, Donald S. ; Williams, Barry W.
Author_Institution :
Electr. Electron. & Comput. Eng., Heriot-Watt Univ., Edinburgh, UK
Abstract :
A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented in this paper. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weights updating algorithm of the BSNN, the appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not required high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme.
Keywords :
electric current control; electric machine analysis computing; machine control; neural nets; reluctance motor drives; torque control; B-spline neural network; closed-loop torque control; current controllers; switched reluctance motor drives; torque estimator; torque ripple reduction; Computer networks; Intelligent networks; Motor drives; Neural networks; Reluctance machines; Reluctance motors; Sensor phenomena and characterization; Spline; Torque control; Torque measurement;
Conference_Titel :
Industry Applications Conference, 2005. Fourtieth IAS Annual Meeting. Conference Record of the 2005
Print_ISBN :
0-7803-9208-6
DOI :
10.1109/IAS.2005.1518845