Motor speed regulation using neural networks

An investigation is conducted of the use of the back-propagation neural network for motion control and speed regulation in industrial servo systems. The goal is to build an intelligent controller or regulator which has a versatility equivalent to that possessed by a human operator. The advantages of neural nets lie in that they are flexible in terms of learning and collective processing capabilities. Simulation was performed to demonstrate the feasibility and effectiveness of the proposed scheme. Network performance as a function of the number of hidden units and the number of training samples is addressed