Adaptive neuro-fuzzy control of the sensorless induction motor drive system

In the paper a model reference adaptive control speed control (MRAC) using on-line trained fuzzy neural network (FNN) was applied to the sensorless induction motor drive system. In this control method fuzzy-logic controller is equipped with additional option for online tuning its chosen parameters. In the paper PI-type fuzzy logic controller is used as the speed controller, in the field oriented control structure, whose connective weights are trained on-line according to the error between the states of the plant and the reference model. The FNN speed controller is on-line tuned to preserve favorable model-following characteristics under various operating conditions. The rotor flux and speed of vector controlled induction motor was estimated using the full-order state observer and speed estimator. The simulation results were verified in the experimental tests, in the wide range of motor speed and parameters changes.