Recently all the moving mechanical parts that are subjected to wear and cause errors in the future are replaced with the equivalent of electrical. A Brushless Direct Current (BLDC) motor is preferable compared to a brushed DC motor because it substitutes the unit of mechanical commutations with an electronic unit, enhancing dynamic properties, noise level, and efficiency. Since it is fairly inexpensive, simple in structure, and performs well, maximum BLDC motor drives use a Proportional-Integral PI controller for controlling the machine's speed. The major issue with the PI controller, on the other hand, is altering its parameters throughout the deployment. As a result, this work shows how to tune the PI controller settings of a BLDC motor drive using Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). The results of a comparison of PSO and GWO for BLDC motors were obtained. Simulation tests for the BLDC engine in MATLAB/Simulink environment show that both PSO and GWO of BLDC motor give good results, but the best is GWO in tested in terms of transient response under different mechanical loads and speeds.
The equivalent of electricity has recently been used to replace all the wear-prone moving mechanical components that produce faults. The electronic unit that substitutes the mechanical commutation unit in Brushless Direct Current (BLDC) motors improves dynamic properties, noise level, and efficiency. This work describes a method for estimating the BLDC machine's rotor speed and position by using Extended Kalman Filter (EKF) and Particle Filter (PF). The BLDC is a non-linear system with nonlinear measurements. To perform the EKF, Jacobian linearization of the motor model and the observation are needed. Linearization leads to a decrease in the accuracy of filter estimation. In PF, the relative likelihood of each particle is computed according to the measurements. Resampling gives set particles are distributed according to power density function (pdf). Then the PF can compute any desired statistical measure of this pdf. A sensorless drive has an accurate good throughout a wide speed range and with varied load torque, according to the simulation's results. The results show that the velocity inaccuracy rate at PF is approximately 0.01% and that at EKF it is approximately 1%. According to the findings, the PF outperformed the EKF in a comparison between them.
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