Sensorless vector control of induction motor using improved self-tuning fuzzy PID controller

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rates for sensorless vector controlled induction motor drives. When induction motor is continuously used long time, its electrical and mechanical parameters would change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. For sensorless vector control, the rotor speed is estimated using MRAS (model reference adaptive system). The proposed scheme is simple in structure and its computational burden is small. The performance of the proposed scheme is evaluated on some experimental studies.