Robust sensorless Induction Motor control based on Extended Kalman Filter observer

Et-taaj, Lamyae; Boulghasoul, Zakaria; Elbacha, Abdelhadi; Kharki, Abdellah El

doi:10.1109/icoten52080.2021.9493502

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…From the nonlinear equations with five state variables: 𝑖 𝑆𝛼 , 𝑖 𝑆𝛽 , 𝜓 𝑅𝛼 , 𝜓 𝑅𝛽 , 𝜔 0 [26], [27], to be able to use the EKF recursively, They need to be transformed from the continuous state equations of the motor into a discrete form, and for each small cycle, these equations are considered linear. Then they are added into the system noise 𝑊 and the measured noise 𝑉, so the EKF algorithm shown below [14]- [16] can be applied.…”

Section: Using Ekf To Estimate the Speed Of Immentioning

confidence: 99%

Comparison of the speedy estimate methods of the induction motors

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Brandštetter

et al. 2022

TELKOMNIKA

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This paper deals with a novel method to achieve the effective performance of the extended Kalman filter (EKF) for the speedy estimate of an induction motor. The real coding genetic algorithm (GA) is used to optimize the components of the covariance matrix in the EKF, thus ensuring the stability and accuracy of the filter in the speed estimation. The advantage of the proposed method is less dependent on the parameters of the induction motor. The content includes the vector control model for induction motor, the speed estimation by modeling the reference frame-model reference adaptive system (RF-MRAS), the current based-model reference adaptive system (CB-MRAS), and the speed estimation with the EKF optimized by genetic algorithm. Simulative studies on the field-oriented controller (FOC) with different operating conditions are performed in Matlab Simulink when the rotor resistance changes in the current speed estimation methods. The simulation results demonstrate the efficiency of the proposed GA-EKF filter compared with other speed estimation methods of induction motors.

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Section: Using Ekf To Estimate the Speed Of Immentioning

confidence: 99%