Speed Sensorless Neuro-Fuzzy Controller for Brush type DC Machines
نویسندگان
چکیده
A speed sensorless neuro-fuzzy controller is proposed for brush type DC motors. The actual speed of the DC machine is estimated using a feed-forward neural network. The inputs of the neural network are the armature current and voltage of the DC machine and their changes in time. Because DC machines are usually fed by 4-quadrant chopper, the measured armature voltage and current contains higher order harmonics, which have reduced value on the output of the neural network. Since the fuzzy controller is a robust system, which tolerates the noisy input to some degree, the observed speed signal is fed to a PI like fuzzy controller. The output of the fuzzy controller is the current reference for the PWM servo system. The proposed neuro-fuzzy controller is robust to the change of load, inertia and speed reference.
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