Genetic and particle swarm optimization algorithms based direct torque control for torque ripple attenuation of induction motor

This paper introduces analysis, control, and comparison of two benchmarking optimization approaches called Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Direct Torque Control (DTC) of a three-phase Induction Motor (IM). This study aims to determine the most efficient and robust of the two different metaheuristic optimization techniques including PID-PSO and PID-GA for DTC of IM. The purpose of the proposed control technique that has been presented is to get over the most significant drawback of DTC, which is a high level of torque output. The issue of torque ripples needs to be reduced to a significant amount using the two proposed control methods PSO-DTC and GA-DTC. As a result, PSO-DTC is the most applicable scheme. The proposed PID-PSO of DTC provided an excellent work performance for IM system drive. The comparison results of the suggested control methods showed a significant improvement of the control system compared to the classical DTC. The result is a high fidelity estimate of electromagnetic torque and speed for computation of motor parameters. A high ripple suppression capability was achieved by the PSO-DTC, which was measured at 22.5 % out of 47.28 % for the traditional approach. Both proposed control schemes were implemented using MATLAB/Simulink platform.