Robust Neural Net-Based Inverse-Model Identification of an Induction Motor

Abstract

Four artificial neural-net training models, Back-propagation, Logicon projection TM, Modular, and Cascade Correlation networks, were used for inversemodel identification of a 3-hp, three-phase induction motor. The fully trained networks were then tested on three similar machines but with distinct characteristics. Results show that the Modular neural network and the Cascade Correlation network were the least sensitive to the differences in the three motors. The two would therefore be considered the best candidates for model identification in adjustable drives when continuous on-line training is undesirable. It is expected that after drive commissioning, the model would maintain an acceptable dynamic and steady state performance through the working life of the motor. This would make the use of expensive on-line self-tuning apparatuses unnecessary for a wide range of adjustable drive applications.