Please use this identifier to cite or link to this item: doi:10.22028/D291-29578
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Title: Robust Electrical and Mechanical Parameter Identification of Low-Power PMSMs
Author(s): König, Niklas
Grasso, Emanuele
Nienhaus, Matthias
Editor(s): Nienhaus, Matthias
Language: English
Title: Innovative Klein- und Mikroantriebstechnik : Beiträge der 11. GMM/ETG-Fachtagung 27.-28. September 2017 in Saarbrücken
Startpage: 92
Endpage: 97
Publisher/Platform: VDE-Verlag
Year of Publication: 2017
Title of the Conference: IKMT-Tagung 2017 - 11. GMM/ETG-Fachtagung
Place of the conference: Saarbrücken, Germany
Publikation type: Conference Paper
Abstract: Parameter identification is an important task in the field of motor control and final stage inspection. Over the years, the demand for robust parameter identification increased especially in the field of highly miniaturized and integrated PMSMs. Nevertheless, current measurements on such kind of motors are characterized by small signal-to-noise ratios, thus limiting the performance of identification algorithms. In this paper, a RLS algorithm is combined with a first order Sliding Mode Differentiator (SMD) used for derivative estimation. This allows the algorithm to estimate the electrical and mechanical parameters of a PMSM while rejecting noise, PWM switching components or quantization effects. Experimental results on a PMSM without and with SMD are shown and compared in terms of estimation performance.
URL of the first publication:
Link to this record: hdl:20.500.11880/28312
ISBN: 978-3-8007-4461-9
Date of registration: 16-Nov-2019
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Matthias Nienhaus
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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