Artificial neural network based adaptive controller for DC motors

Abstract

This thesis studies the development, implementation, and performance of an on-line self-tuning artificial neural network (ANN) based adaptive speed controller for a permanent magnet DC motor. For more accurate speed control, an on-line training algorithm with an adaptive learning rate is introduced, rather than using fixed weights and biases for the ANN. Both analytical and practical details of the development and implementation of the ANN based adaptive controller techniques are systematically presented. The complete system is implemented in real time using a host-target prototyping environment and a laboratory PM (permanent-magnet) DC motor. To validate its efficiency, the performance of the proposed ANN-based adaptive controller was compared with proportional-integral-derivative (PID) and proportional-integral (PI)-controller-based PM DC motor drive systems under different operating conditions. The experimental results show that the ANN based adaptive controller is robust, accurate, and insensitive to parameter variations and load disturbances