About: FPGA implementation of marginalized particle filter for sensorless control of PMSM drives

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: FPGA implementation of marginalized particle filter for sensorless control of PMSM drives Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6700510&queryText%3DFPGA+implementation+of+marginalized+particle+filter+for+sensorless+control+of+PMSM+drives
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. In effect, this allows to represent accurately the inherent non-Gaussianity and nonlinearity of the model. This allows estimation of the rotor position of the PMSM drive in the full speed range, including the standstill. The main disadvantage is its high computational cost. In this paper, we present an implementation of the marginalized particle filter in the field programmable logic array (FPGA). The parallel nature of the MPF algorithm allows to use pipelining which yields speedup in the order of magnitude in comparison to the DSP implementatio n. The sensorless control of the drive is implemented on a board with both DSP and FPGA, where the drive control runs on the DSP and the MPF estimator in the FPGA. Execution time of the estimator is thus negligible in the execution time of the sensorless control. Performance of the resulting sensorless control algorithm is evaluated on a developed drive prototype of rated power of 10.7kW. 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. In effect, this allows to represent accurately the inherent non-Gaussianity and nonlinearity of the model. This allows estimation of the rotor position of the PMSM drive in the full speed range, including the standstill. The main disadvantage is its high computational cost. In this paper, we present an implementation of the marginalized particle filter in the field programmable logic array (FPGA). The parallel nature of the MPF algorithm allows to use pipelining which yields speedup in the order of magnitude in comparison to the DSP implementatio n. The sensorless control of the drive is implemented on a board with both DSP and FPGA, where the drive control runs on the DSP and the MPF estimator in the FPGA. Execution time of the estimator is thus negligible in the execution time of the sensorless control. Performance of the resulting sensorless control algorithm is evaluated on a developed drive prototype of rated power of 10.7kW. (en)
Title	FPGA implementation of marginalized particle filter for sensorless control of PMSM drives FPGA implementation of marginalized particle filter for sensorless control of PMSM drives (en)
skos:prefLabel	FPGA implementation of marginalized particle filter for sensorless control of PMSM drives FPGA implementation of marginalized particle filter for sensorless control of PMSM drives (en)
skos:notation	RIV/49777513:23220/13:43919611!RIV14-MSM-23220___
http://linked.open...avai/predkladatel	Fakulta elektrotechnická
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED2.1.00/03.0094), P(GAP102/11/0437)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Košan, Tomáš Nedvěd, Robert Šmídl, Václav Peroutka, Zdeněk
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	75616
http://linked.open...ai/riv/idVysledku	RIV/49777513:23220/13:43919611
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	sensorless control; marginalized particle filter; Field programmable gate array (en)
http://linked.open.../riv/klicoveSlovo	marginalized particle filter sensorless control Field programmable gate array
http://linked.open...ontrolniKodProRIV	[27526B2BE596]
http://linked.open...v/mistoKonaniAkce	Vienna
http://linked.open...i/riv/mistoVydani	Vienna
http://linked.open...i/riv/nazevZdroje	Proceedings of the IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society
http://linked.open...in/vavai/riv/obor	JA
http://linked.open...ichTvurcuVysledku	4 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	Regional Innovation Centre for Electrical Engineering (RICE) Control and Parameter Identification of AC Electric Drives under Critical Operating Conditions
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Košan, Tomáš Peroutka, Zdeněk Šmídl, Václav Nedvěd, Robert
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-11-10 (xsd:date)
issn	1553-572X
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/IECON.2013.6700510
http://purl.org/ne...btex#hasPublisher	IEEE
https://schema.org/isbn	978-1-4799-0224-8
http://localhost/t...ganizacniJednotka	23220

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 114 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software