Title :
Implementation of a neural network to adaptively identify and control VSI-Fed induction motor stator currents
Author :
Burton, Bruce ; Harley, Ronald G. ; Diana, Gregory ; Rodgerson, James L.
Author_Institution :
Dept. of Electr. Eng., Natal Univ., Durban, South Africa
Abstract :
This paper presents a prototype hardware implementation of a continually online trained artificial neural network (ANN) to adaptively identify the electrical dynamics of an induction machine and control its stator currents from a pulsewidth modulated voltage-source inverter. A single-transputer-based hardware platform is described, and the effects of computational speed limitations on the controller bandwidth are discussed. Captured results are compared with simulation results to practically verify the success of the adaptive neural network identification and control scheme
Keywords :
PWM invertors; adaptive control; identification; induction motors; learning (artificial intelligence); machine control; neurocontrollers; power engineering computing; VSI-fed induction motor; adaptive identification; adaptive neural network; controller bandwidth; electrical dynamics; neural network; online trained artificial neural network; pulsewidth modulated voltage-source inverter; single-transputer-based hardware platform; stator currents; Artificial neural networks; Induction machines; Neural network hardware; Neural networks; Prototypes; Pulse inverters; Pulse modulation; Pulse width modulation inverters; Stators; Voltage control;
Journal_Title :
Industry Applications, IEEE Transactions on
