Improvement direct torque control of induction motor using robust intelligence artificial ANFIS speed controller

This paper proposes a study aimed at improving the conventional direct torque control (DTC) technique applied to induction motors (IM). The primary aim is to reduce the harmonic distortions and fluctuations associated with the electrical current, flux variations, and generated torque, while ensuring accurate speed reference tracking and ensuring optimal dynamic performance of the drive, especially under variable speed conditions. To achieve this, we introduce an intelligent control system that utilizes a hybrid neuro-fuzzy inference model (ANFIS), through the application of the back propagation method. The DTC-ANFIS technique is compared with the traditional DTC-PI method and simulated using MATLAB/Simulink in different scenarios. The obtained results reveal a significant improvement in performance over DTC-PI, with superior results over a wide speed range.