Title :
Modified LMS adaptive algorithm for CMAC neural network based control of switched reluctance motors
Author :
Shang, C. ; Reay, D.S. ; Williams, B.W.
Author_Institution :
Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK
fDate :
6/6/1996 12:00:00 AM
Abstract :
A novel approach to adapting the weights of a CMAC neural network for torque ripple reduction in switched reluctance motors is proposed, using a variable learning rate function within the standard LMS algorithm. Simulation results demonstrate that training CMAC networks following this approach affords low torque ripple with high power efficiency
Keywords :
cerebellar model arithmetic computers; least mean squares methods; machine control; neurocontrollers; reluctance motors; CMAC neural network; LMS adaptive algorithm; control; learning rate function; power efficiency; simulation; switched reluctance motor; torque ripple; training;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19960721
