LAPSE:2018.1185
Published Article
LAPSE:2018.1185
State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF
Bo Xu, Fangqiang Mu, Guoding Shi, Wei Ji, Huangqiu Zhu
December 3, 2018
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 the state forecast covariance square root matrix, which can realize the residuals orthogonality and force the SRUKF to track the real state rapidly. The theoretical analysis of the improved SRUKF and implementation details for PMSM state estimation are examined. The simulation results show that the improved SRUKF has higher nonlinear approximation accuracy, stronger numerical stability and computational efficiency, and it is an effective and powerful tool for PMSM state estimation under the conditions of step response or load disturbance.
Keywords
permanent magnet synchronous motor, square root unscented Kalman filter, state estimation
Suggested Citation
Xu B, Mu F, Shi G, Ji W, Zhu H. State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF. (2018). LAPSE:2018.1185
Author Affiliations
Xu B: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Mu F: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Shi G: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Ji W: Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China
Zhu H: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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Journal Name
Energies
Volume
9
Issue
7
Article Number
E489
Year
2016
Publication Date
2016-06-24
Published Version
ISSN
1996-1073
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PII: en9070489, Publication Type: Journal Article
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LAPSE:2018.1185
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doi:10.3390/en9070489
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Dec 3, 2018
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Calvin Tsay
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