Trajectory control of DC shunt motor by NARMA Level-2 neuro controller

This paper proposes an intelligent speed controller to the trajectory control of shunt-connected DC motor based on Nonlinear Autoregressive Moving Average Level-2(NARMA L-2) technique. By adjusting the weights of a neural network with respect to variation between the actual speed and command speed the armature rotor speed is tracked. Based on the dynamic mathematical model of motor, PID and NARMA L-2 controller responses are analyzed. The speed response of motor is observed by giving reference inputs as speed and load torque in terms of step variation. By comparing the motor response of conventional PID and NARMA L-2 neuro controller, it was observed that NARMA L-2 neuro controller exhibits better performance in terms of settling time, peak over shoot, steady state error.