The Self-Tuning Neural Speed Regulator Applied to DC Servo Motor

This study utilizes the direct neural control (DNC) based on back propagation neural networks (BPN) with specialized learning architecture applied to regulate the speed of a DC servo motor. The proposed neural controller is treated as a speed regulator to keep the motor in constant speed without the specified reference model. A tangent hyperbolic function is used as the activation function, and the back propagation error is approximated by a linear combination of error and error´s differential. The simulation and experiment results reveal that the proposed speed regulator keeps motor in constant speed with high convergent speed, and enhances the adaptability of the accurate speed control system.