An Efficient IWOLRS Control Technique of Brushless DC Motor for Torque Ripple Minimization

This manuscript proposes an improved DC-DC converter framework using hybrid control algorithm for minimizing brushless DC motor (BLDC) torque ripple (TR). At first, the modeling of the brushless DC motor is intended by an enhanced Cuk converter (ECC). The function and performance of the Cuk converter are updated using application of switched inductor. In this way, the control system integrates two control loops such as speed and torque control loop, which is employed for improving BLDC performance. Therefore, the Invasive Weed Optimization (IWO) and Local Random Search (LRS) are proposed to enhance control loop operations. In the IWO algorithm, the LRS approach is used as part of the dispersion process to build up the course of action to find precision. This manuscript explores the IWO-LRS algorithm for limiting BLDC motor speed and torque error. Nevertheless, the exit from the proposed approach is subject to the speed and torque controller input. The better optimal gain parameters have been worked out for the update of the controller operation through the aid of necessary goal functions. The proposed controller topology is activated in MATLAB/Simulink site and the performance is evaluated using other existing methods, like Particle Swarm Optimization (PSO), Bacterial Foraging (BF) algorithm.