Torque ripple reduction of Induction Motor using Dynamic Fuzzy Prediction Direct Torque Control

This paper presents a Dynamic Fuzzy Logic based Prediction Direct Torque Control (DFL-PDTC) technique to reduce torque ripples within the squirrel cage induction motor (SCIM). This DFL and PDTC combination reduces the high amount of torque ripple and improves the starting conditions in both low& high mechanical speeds of the motor. The Induction Motor (IM) is used mainly in industrial applications due to its limited size and self-starting performance than other motors. The dynamic behavior of the SCIM is investigated in terms of a parameter such as torque, speed, and flux. In this work, a fuzzy logic controller (FLC) with the Gaussian membership function (GMFs) is used to generate an optimal control pulse, which controls the speed of the induction motor. Depending on the GMFs parameter, the motor speed is controlled and the number of torque ripples is decreased. Furthermore, PDTC selects the suitable voltage vector by analyzing the comparator output of Torque and flux and select the best one using the optimization method. In the proposed work, a Flower Pollination Algorithm (FPA) is used to optimize the GMFs parameter (α,c) in the controller, which reduces the number of ripples by selecting the best value (optimized). Whenever the motor runs, this optimized value is set as a fixed value to reduce the torque ripples. The experimental result indicates that the proposed optimization algorithm reduces the torque ripple using dynamic fuzzy controller and PDTC control techniques. The proposed method is implemented in the MATLAB/Simulink platform and the performance analysis highlights the efficiency of the suggested approach.