Trajectory training of feedforward neural networks for DC motor speed control

Abstract

This work discusses the use of template trained neural networks for DC motor speed control. It proposes the use of input-output trajectories to train the neural network instead of using data points as is normally done for feedforward neural networks. Experimental results show that the trajectory trained neural network can successfully perform speed control of PMDC motors when compared to neural networks trained using conventional training methods.