Using a Bivariate Polynomial in an EKF for State and Inductance Estimations in the Presence of Saturation Effects to Adaptively Control a PMSM
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In: IEEE Access, Vol. 10, 19.10.2022, p. 111545-111553.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Using a Bivariate Polynomial in an EKF for State and Inductance Estimations in the Presence of Saturation Effects to Adaptively Control a PMSM
AU - Zwerger, Tanja
AU - Mercorelli, Paolo
N1 - This publication was funded by the Open Access Publication Fund of Leuphana University Lüneburg. Publisher Copyright: © 2013 IEEE.
PY - 2022/10/19
Y1 - 2022/10/19
N2 - This paper takes into consideration a combined extended Kalman filter (CEKF) by using a bivariate polynomial for the estimation of Ld and Lq in saturation conditions. In the context of the Kalman filter (KF), Ld and Lq are modelled as nonlinear augmented states to control a permanent magnetic synchronous machine (PMSM). Once Ld and Lq are estimated, continuous monitoring of the machine saturation conditions is achieved to ensure the desired torque even under saturation conditions. The proposed adaptive control method based on maximum torque per ampere (MTPA) consists of an adaptive feedforward and PI controller. A discussion in light of the measured results using Hardware-in-the-loop is also included.
AB - This paper takes into consideration a combined extended Kalman filter (CEKF) by using a bivariate polynomial for the estimation of Ld and Lq in saturation conditions. In the context of the Kalman filter (KF), Ld and Lq are modelled as nonlinear augmented states to control a permanent magnetic synchronous machine (PMSM). Once Ld and Lq are estimated, continuous monitoring of the machine saturation conditions is achieved to ensure the desired torque even under saturation conditions. The proposed adaptive control method based on maximum torque per ampere (MTPA) consists of an adaptive feedforward and PI controller. A discussion in light of the measured results using Hardware-in-the-loop is also included.
KW - bivariate polynomial
KW - extended Kalman filter
KW - parameter estimation
KW - PMSM
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85140780812&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3215511
DO - 10.1109/ACCESS.2022.3215511
M3 - Journal articles
AN - SCOPUS:85140780812
VL - 10
SP - 111545
EP - 111553
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -