Open AccessBBO-Based State Optimization for PMSM Machines
Author(s) -
Hanane Lakehal,
Mouna Ghanai,
Kheireddine Chafaa
Publication year - 2021
Publication title -
vietnam journal of computer science
Language(s) - English
Resource type - Journals
eISSN - 2196-8888
pISSN - 2196-8896
DOI - 10.1142/s2196888822500026
Subject(s) - extended kalman filter , control theory (sociology) , robustness (evolution) , computer science , kalman filter , stator , particle swarm optimization , torque , rotor (electric) , covariance , estimator , control engineering , engineering , mathematics , algorithm , artificial intelligence , physics , mechanical engineering , biochemistry , chemistry , thermodynamics , control (management) , gene , statistics
In this investigation, state vector estimation of the Permanent Magnet Synchronous machine (PMSM) using the nonlinear Kalman estimator (Extended Kalman Filter) is considered. The considered states are the speed of the rotor, its angular position, the torque of the load and the resistance of the stator. Since the extended Kalman filter contains some free parameters, it will be necessary to optimize them in order to obtain a better efficiency. The free parameters of EKF are the covariance matrices of state noise and measurement noise. These later will be auto adjusted by a new metaheuristic optimization technique called Biogeographical-based optimization (BBO). As far as we know, BBO–EKF optimization for PMSM state was not treated in the literature. The suggested estimation tuning approach is demonstrated using a computer simulation of a PMSM. Simulated experimentations show the robustness and effectiveness of the proposed scheme. In addition, a detailed comparative study with conventional methods like Particle Swarm Optimization and Genetic Algorithms will be given.