2016
DOI: 10.3390/en9070489
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State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

Abstract: This paper focuses on an improved square root unscented Kalman filter (SRUKF) and its application for rotor speed and position estimation of permanent magnet synchronous motor (PMSM). The approach, which combines the SRUKF and strong tracking filter, uses the minimal skew simplex transformation to reduce the number of the sigma points, and utilizes the square root filtering to reduce computational errors. The time-varying fading factor and softening factor are introduced to self-adjust the gain matrices and th… Show more

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Cited by 15 publications
(10 citation statements)
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“…, 2n. When the system meets the Gaussian noise assumptions, α, β and κ in the UKF approach are typically set as 10 −3 , 2 and 0 respectively [36].…”
Section: Unscented Kalman Filtermentioning
confidence: 99%
“…, 2n. When the system meets the Gaussian noise assumptions, α, β and κ in the UKF approach are typically set as 10 −3 , 2 and 0 respectively [36].…”
Section: Unscented Kalman Filtermentioning
confidence: 99%
“…One is for the error covariance matrix, which may be negative on Cholesky decomposition. Here are some methods for this problem such as SR decomposition [13], singular value decomposition (SVD) [14], and adaptive noise variance [15]. SVD is more robust than SR decomposition and less complex than the adaptive noise variance method, so it is adopted in this study [16].…”
Section: Introductionmentioning
confidence: 99%
“…Eliminating the external rotor excitation removes the rotor losses, which improves the PMSM efficiency dramatically. Moreover, the absence of rotor winding eliminates not only the slip rings but also the brushes, which leads to a considerable reduction of the maintenance costs [1][2][3][4][5][6].…”
Section: Introductionmentioning
confidence: 99%