Dual Kalman Filters Analysis for Interior Permanent Magnet Synchronous Motors
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Dual Kalman Filters Analysis for Interior Permanent Magnet Synchronous Motors. / Zwerger, Tanja; Mercorelli, Paolo.
Advanced, Contemporary Control : Proceedings of KKA 2020—The 20th Polish Control Conference, Łódź, Poland, 2020. ed. / Andrzej Bartoszewicz; Jacek Kabziński; Janusz Kacprzyk. Vol. 1 Cham : Springer, 2020. p. 424-435 (Advances in Intelligent Systems and Computing; Vol. 1196).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Dual Kalman Filters Analysis for Interior Permanent Magnet Synchronous Motors
AU - Zwerger, Tanja
AU - Mercorelli, Paolo
N1 - Conference code: 20
PY - 2020/1/1
Y1 - 2020/1/1
N2 - This paper deals with an analysis and design of Dual Extended Kalman Filters (DKFs) to estimate parameters and state variables in Permanent Magnet Synchronous Machines (PMSMs) to be utilized in a control structure. A dual estimation problem consists of a simultaneous estimation of states of the dynamical system and its parameters using only noisy output observations. In this paper, the limit of an Augmented and Extended Kalman Filter (AEKF) obtained through standard state augmentation to estimate parameters is shown and, alternatively, a DKF approach which is characterized by the use of the state model descriptions in the output of an AEKF is proposed. The two different approaches are analyzed and compared. These results are supported by simulations.
AB - This paper deals with an analysis and design of Dual Extended Kalman Filters (DKFs) to estimate parameters and state variables in Permanent Magnet Synchronous Machines (PMSMs) to be utilized in a control structure. A dual estimation problem consists of a simultaneous estimation of states of the dynamical system and its parameters using only noisy output observations. In this paper, the limit of an Augmented and Extended Kalman Filter (AEKF) obtained through standard state augmentation to estimate parameters is shown and, alternatively, a DKF approach which is characterized by the use of the state model descriptions in the output of an AEKF is proposed. The two different approaches are analyzed and compared. These results are supported by simulations.
KW - Augmented Extended Kalman Filter
KW - Dual Extended Kalman Filter
KW - Parameters estimation
KW - Permanent Magnet Synchronous Machine
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85088209353&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50936-1_36
DO - 10.1007/978-3-030-50936-1_36
M3 - Article in conference proceedings
AN - SCOPUS:85088209353
SN - 978-3-030-50935-4
VL - 1
T3 - Advances in Intelligent Systems and Computing
SP - 424
EP - 435
BT - Advanced, Contemporary Control
A2 - Bartoszewicz, Andrzej
A2 - Kabziński, Jacek
A2 - Kacprzyk, Janusz
PB - Springer
CY - Cham
T2 - 20th Polish Control Conference, PCC 2020
Y2 - 22 June 2020 through 24 June 2020
ER -