Open AccessPosition and Speed Estimation of PMSM Based on Extended Kalman Filter Tuned by Biogeography-Based-Optimization
Author(s) -
Samia Allaoui,
Yahia Laamari,
Kheireddine Chafaa,
Salah Saad
Publication year - 2021
Publication title -
journal européen des systèmes automatisés/journal européen des systèmes automaitsés
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.16
H-Index - 20
eISSN - 2116-7087
pISSN - 1269-6935
DOI - 10.18280/jesa.540405
Subject(s) - extended kalman filter , control theory (sociology) , covariance , kalman filter , computer science , position (finance) , convergence (economics) , particle swarm optimization , noise (video) , invariant extended kalman filter , mathematics , algorithm , artificial intelligence , economics , image (mathematics) , economic growth , statistics , control (management) , finance
In a sensorless control of PMSM based on Extended Kalman Filter (EKF), the correct selection of system and measurement noise covariance has a great influence on the estimation performances of the filter. In fact, it is extremely difficult to find their optimal values by trial and error method. Therefore, the main contribution of this work is to prove the efficiency of Biogeography-Based-Optimization (BBO) technique to obtain the optimal noise covariance matrices Q and R. The BBO and EKF combination gives a BBO-EKF algorithm, which allows to estimate all the state variables of PMSM drive particularly, the rotor position and speed. In this paper, three evolutionary algorithms namely Particle Swarm Optimization (PSO), genetic algorithms (GAs) and BBO are used to get the best Q and R of EKF. Simulations tests performed in Matlab Simulink environment show excellent performance of BBO-EKF compared to GAs-EKF and PSO-EKF approaches either in resolution or in convergence speed.