Marginalized particle filter for sensorless control of PMSM drives

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Marginalized particle filter for sensorless control of PMSM drives

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Marginalized particle filter for sensorless control of PMSM drives

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

—

Projekt

<a href="/cs/project/GAP102%2F11%2F0437" target="_blank" >GAP102/11/0437: Regulace a identifikace parametrů střídavých elektrických pohonů v kritických provozních stavech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

Počet stran výsledku

6

Strana od-do

1877-1882

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Montreal

Datum konání akce

25. 10. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000316962901144