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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Using a Bivariate Polynomial in an EKF for State and Inductance Estimations in the Presence of Saturation Effects to Adaptively Control a PMSM. / Zwerger, Tanja; Mercorelli, Paolo.
In: IEEE Access, Vol. 10, 19.10.2022, p. 111545-111553.

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@article{31233b8a6b7b40e795498b2ba702c8e8,
title = "Using a Bivariate Polynomial in an EKF for State and Inductance Estimations in the Presence of Saturation Effects to Adaptively Control a PMSM",
abstract = "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. ",
keywords = "bivariate polynomial, extended Kalman filter, parameter estimation, PMSM, Engineering",
author = "Tanja Zwerger and Paolo Mercorelli",
note = "This publication was funded by the Open Access Publication Fund of Leuphana University L{\"u}neburg. Publisher Copyright: {\textcopyright} 2013 IEEE.",
year = "2022",
month = oct,
day = "19",
doi = "10.1109/ACCESS.2022.3215511",
language = "English",
volume = "10",
pages = "111545--111553",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

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 -