Open AccessSpeed estimation of PMSM using SRUKF algorithm
Author(s) -
Jingnan Li,
Yuan Gao,
Su Young Hong,
Yin Zhang
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/5/052013
Subject(s) - cholesky decomposition , kalman filter , control theory (sociology) , linearization , convergence (economics) , stability (learning theory) , computer science , truncation (statistics) , nonlinear system , unscented transform , algorithm , extended kalman filter , covariance , mathematics , invariant extended kalman filter , control (management) , eigenvalues and eigenvectors , artificial intelligence , physics , statistics , quantum mechanics , machine learning , economics , economic growth
Speed estimation is a key technology to realize sensorless control for the PMSM. Based on the unscented Kalman filter (UKF) method without linearization of nonlinear system equations, this letter provides square root unscented Kalman filter (SRUKF) algorithm that operates through iterating the square roots of the covariance matrixes obtained by QR decomposition and Cholesky decomposition. The presented method can further improve speed estimation performance through decreasing the effect of truncation error and enhancing the convergence and stability of algorithm. Simulation results of sensorless control system demonstrate the feasibility and effectiveness of the proposed algorithm.