Title :
Identification and control of induction motors using artificial neural networks with random weight change training
Author :
Burton, Bruce ; Harley, Ronald G. ; Habetler, Thomas G.
Author_Institution :
Dept. of Electr. Eng., Natal Univ., Durban, South Africa
Abstract :
This paper highlights some of the latest developments in ongoing work on the practical implementation of a continually online trained neural network induction motor controller. In particular, it considers the potential use of a proposed analogue VLSI sigmoidal feedforward neural network, with on-chip random weight change training, for rapid control of induction motor stator currents. The random weight change training algorithm is described and its convergence mechanism explained by comparison with that of backpropagation training. Simulation results also demonstrate the potential induction motor current control performance achievable with random weight change in comparison with that of backpropagation training
Keywords :
control system analysis; control system synthesis; electric current control; feedforward neural nets; induction motors; learning (artificial intelligence); machine control; machine testing; machine theory; neurocontrollers; stators; velocity control; analogue VLSI sigmoidal feedforward neural network; control design; control performance; control simulation; convergence mechanism; induction motor controller; motor speed control; online trained neural network; random weight change training algorithm; stator current control; Artificial neural networks; Backpropagation algorithms; Convergence; Current control; Feedforward neural networks; Induction motors; Network-on-a-chip; Neural networks; Stators; Very large scale integration;
Conference_Titel :
AFRICON, 1996., IEEE AFRICON 4th
Conference_Location :
Stellenbosch
Print_ISBN :
0-7803-3019-6
DOI :
10.1109/AFRCON.1996.563001
