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In this paper, a new filtering algorithm is proposed for system control of the sensorless BLDC motor based on the Ensemble Kalman filter (EnKF). The proposed EnKF algorithm is used to estimate the speed and rotor position of the BLDC motor only using the measurements of terminal voltages and three-phase currents. The speed estimation performance of...
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... Generally , EKF's estimating system is divided into two stages: the step of prediction and the step of correction The predicted state variable value and predicted state variable value are calculated in the first stage. The expected state covariance Matrix is denoted by − 1, can be obtained, and a correction term is added to the anticipated value in the second stage, the corrective step ̃/ [18][19][20][21][22][23][24]. ...
... The estimation procedures for the EKF can be listed as follow [21,22]: ...
... That is, we compute a brand new set of particles . + that are randomly generated on the basis of the relative likelihoods qi [21]. Figure 5 shows the steps for implementing a Particle Filter. ...
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.
... The induced backs EMFs have a trapezoidal form and their peak values are equal to λm w. Electromagnetic torque could be calculated through [10]: ...
... Omega wolves use alpha, beta, and delta sites to update their sites. The first ideal solution is alpha, while 2 nd and 3 rd ideal solutions are respectively (beta) and (delta) [7][8][9][10][11][12][13][14][15][16]. ...
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.
In this paper, a technique for estimation of the rotor speed and position of a BLDC motor is presented. BLDC motors are nonlinear system and to control BLDC, rotor position must be known. This is usually done by using sensors that result in increased costs, the size of the motor and reduces reliability. In this research, the rotor speed and rotor position of BLDC motor are estimated by Ensemble Kalman Filter only use the stator line voltage and current measurement. The Ensemble Kalman Filter (EnKF) is a recursive filter suitable for non-linear systems. The estimated rotor speed and position rotor has been used for the motor drive and closed loop speed control of the BLDC motor. The performance of the proposed technique is demonstrated through real-time simulation.
