Marginalized particle filter for sensorless control of PMSM drives

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F12%3A43916873" target="_blank" >RIV/49777513:23220/12:43916873 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6388915" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6388915</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IECON.2012.6388915" target="_blank" >10.1109/IECON.2012.6388915</a>

Result language
angličtina
Original language name
Marginalized particle filter for sensorless control of PMSM drives
Original language description
Marginalized particle filter is a stochastic filter combining Kalman filters with particle filters. It decomposes the model into linear and nonlinear part and applies the Kalman filter for the former and the particle filter for the latter. Its application in sensorless control of permanent magnet synchronous motor (PMSM) drives is based on separate treatment of the state variables: the rotor position is represented by a set of samples (particles), and the rotor speed is estimated by the Kalman filters associated with each sample. In effect, this allows to represent accurately the inherent non-Gaussianity and nonlinearity of the model. We show that the resulting filter is capable to estimate the rotor position in the full speed range, including the standstill. Analysis of the filter performance is presented on open-loop off-line analysis of data recorded on a drive prototype. Execution time of optimized implementation of the algorithm for six particles in DSP is comparable to that of th
Czech name
—
Czech description
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Project
<a href="/en/project/GAP102%2F11%2F0437" target="_blank" >GAP102/11/0437: Control and Parameter Identification of AC Electric Drives under Critical Operating Conditions</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society and 2012 6th IEEE International Conference on E-Learning in Industrial Electronics (ICELIE) Montreal, Canada
ISBN
978-1-4673-2421-2
ISSN
1553-572X
e-ISSN
—
Number of pages
6
Pages from-to
1877-1882
Publisher name
IEEE
Place of publication
New York
Event location
Montreal
Event date
Oct 25, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000316962901144

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