Sensor-less Predictive Control of Induction Motor with Super-Twisting Sliding Observer and Fuzzy Adaptive Estimator

Induction motor is one of the most widely used electric motors in the industry and even in everyday life. Various controllers are provided for this motor, one of which is the predictive control method. The main problem with this new method is the inaccuracy of the motor parameters and the lack of access to accurate system information. This inaccuracy can lead to errors in the controller operation. In this paper, the predictive control of induction motors is considered. To improve the performance of this controller, a super twisting sliding observer is used to reduce the effect of uncertainties in the structure of the induction motor. A model reference adaptive estimator has also been used to improve speed estimation in sensor-less control and online estimation of stator resistance. Finally, by adding a fuzzy controller, improvements in the performance of this estimator have been achieved. The simulation results indicate the optimal performance of this method in eliminating the uncertain effect and optimal control of the induction motor.