Title :
FPGA-based space vector PWM with Artificial Neural Networks
Author :
Osorio, John ; Ponce, Pedro ; Molina, Arturo
Author_Institution :
Res. Dept., Tecnol. de Monterrey-ITESM, Mexico City, Mexico
Abstract :
This article presents the improvement of a PWM technique, called Space Vector PWM (SVPWM), using an Artificial Neural Network (ANN) to minimize the mathematic complexity involved with the SVPWM. The latter is a pulse-width modulation technique that is wide implemented to control AC electric motors. The results obtained from this research work will be used for further implementation of artificial intelligence techniques to control electric vehicle powertrains. Matlab is implemented for the ANN design and Labview for the FPGA programming and implementation.
Keywords :
AC motors; electric vehicles; field programmable gate arrays; machine control; neural nets; power transmission (mechanical); pulse width modulation; AC electric motor control; ANN design; FPGA programming; FPGA-based space vector PWM; Labview; Matlab; PWM technique; SVPWM; artificial intelligence techniques; artificial neural networks; electric vehicle powertrain control; mathematical complexity; pulse-width modulation technique; Artificial neural networks; Field programmable gate arrays; Induction motors; Space vector pulse width modulation; Vectors; AC Motors; ANN; Artificial Intelligence; FPGA; SVPWM;
Conference_Titel :
Electrical Engineering, Computing Science and Automatic Control (CCE), 2012 9th International Conference on
Conference_Location :
Mexico City
Print_ISBN :
978-1-4673-2170-9
DOI :
10.1109/ICEEE.2012.6421215
