Speed control of AC servo motor using neural networks

Recently intelligent control has received increased attention in various applications. With PID controllers, it is difficult to obtain gain suitable to the load because of disturbances, parameter variations and measurement noise. In order to reduce mechanical parameter variations, and to obtain good dynamic performance, PID controllers and neural network controllers for AC servomotor control are investigated. The microprocessor provides an output to the difference in command. The servo system improves the characteristics of speed control. When the motor is running at the same speed as set by the reference signal, the speed encoder also provides a signal of the same frequency. From the viewpoint of wide control range, a good dynamic performance and a cost effective approach, the NNE of an adaptive neural network controller is used to identify the parameters and characteristics of the AC servomotor. To train the controller, the weights are dynamically adjusted using the backpropagation algorithm. Neural networks are used so PID control can control, efficiently, the speed of the AC servomotor. Finally experimental results prove the excellent performance of this control system.