VARMAX-based closed-loop subspace model identification
In this paper a predictor-based subspace model identification method is presented that relaxes the requirement that the past window has to be large for asymptotical consistent estimates. By utilizing a VARMAX model, a finite description of the input-output relation is formulated. An extended least s...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3375 |
|---|---|
| container_issue | |
| container_start_page | 3370 |
| container_title | |
| container_volume | |
| creator | Houtzager, I. van Wingerden, J.-W. Verhaegen, M. |
| description | In this paper a predictor-based subspace model identification method is presented that relaxes the requirement that the past window has to be large for asymptotical consistent estimates. By utilizing a VARMAX model, a finite description of the input-output relation is formulated. An extended least squares recursion is used to estimate the Markov parameters in the VARMAX model set. Using the Markov parameters the state sequence can be estimated and consequently the system matrices can be recovered. The effectiveness of the proposed method in comparison with an existing method is emphasized with a simulation study on a wind turbine model operating in closed loop. |
| doi_str_mv | 10.1109/CDC.2009.5400695 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5400695</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5400695</ieee_id><sourcerecordid>5400695</sourcerecordid><originalsourceid>FETCH-LOGICAL-c222t-ae9a8c001b2b5d95f34d1841d21af0fad3a1b5d222d9a7d511617025717f25a83</originalsourceid><addsrcrecordid>eNpVkEtLw0AUhUe0YK3dC27yBybeO8_MMsQnVARRcVduMjMwkjahExf-ewN24-rjcD7O4jB2hVAigrtpbptSALhSKwDj9AlbO1uhEkrJygp5-i-jOWNLQIdcCDQLtrSOGwXO4Dm7yPkLACowZsnMR_36XH_ylnLwRdcPM3g_DGORv9s8UheK3eBDXyQf9lOKqaMpDftLtojU57A-csXe7-_emke-eXl4auoN74QQE6fgqOoAsBWt9k5HqTxWCr1AihDJS8K5mF3vyHqNaNCC0BZtFJoquWLXf7sphLAdD2lHh5_t8QL5C9vhSVA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>VARMAX-based closed-loop subspace model identification</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Houtzager, I. ; van Wingerden, J.-W. ; Verhaegen, M.</creator><creatorcontrib>Houtzager, I. ; van Wingerden, J.-W. ; Verhaegen, M.</creatorcontrib><description>In this paper a predictor-based subspace model identification method is presented that relaxes the requirement that the past window has to be large for asymptotical consistent estimates. By utilizing a VARMAX model, a finite description of the input-output relation is formulated. An extended least squares recursion is used to estimate the Markov parameters in the VARMAX model set. Using the Markov parameters the state sequence can be estimated and consequently the system matrices can be recovered. The effectiveness of the proposed method in comparison with an existing method is emphasized with a simulation study on a wind turbine model operating in closed loop.</description><identifier>ISSN: 0191-2216</identifier><identifier>ISBN: 9781424438716</identifier><identifier>ISBN: 1424438713</identifier><identifier>EISBN: 9781424438723</identifier><identifier>EISBN: 1424438721</identifier><identifier>DOI: 10.1109/CDC.2009.5400695</identifier><identifier>LCCN: 79-640961</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptive control ; Autoregressive processes ; Information retrieval ; Least squares approximation ; MIMO ; Parameter estimation ; Predictive models ; Recursive estimation ; State estimation ; Wind turbines</subject><ispartof>Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009, p.3370-3375</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c222t-ae9a8c001b2b5d95f34d1841d21af0fad3a1b5d222d9a7d511617025717f25a83</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5400695$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5400695$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Houtzager, I.</creatorcontrib><creatorcontrib>van Wingerden, J.-W.</creatorcontrib><creatorcontrib>Verhaegen, M.</creatorcontrib><title>VARMAX-based closed-loop subspace model identification</title><title>Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference</title><addtitle>CDC</addtitle><description>In this paper a predictor-based subspace model identification method is presented that relaxes the requirement that the past window has to be large for asymptotical consistent estimates. By utilizing a VARMAX model, a finite description of the input-output relation is formulated. An extended least squares recursion is used to estimate the Markov parameters in the VARMAX model set. Using the Markov parameters the state sequence can be estimated and consequently the system matrices can be recovered. The effectiveness of the proposed method in comparison with an existing method is emphasized with a simulation study on a wind turbine model operating in closed loop.</description><subject>Adaptive control</subject><subject>Autoregressive processes</subject><subject>Information retrieval</subject><subject>Least squares approximation</subject><subject>MIMO</subject><subject>Parameter estimation</subject><subject>Predictive models</subject><subject>Recursive estimation</subject><subject>State estimation</subject><subject>Wind turbines</subject><issn>0191-2216</issn><isbn>9781424438716</isbn><isbn>1424438713</isbn><isbn>9781424438723</isbn><isbn>1424438721</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkEtLw0AUhUe0YK3dC27yBybeO8_MMsQnVARRcVduMjMwkjahExf-ewN24-rjcD7O4jB2hVAigrtpbptSALhSKwDj9AlbO1uhEkrJygp5-i-jOWNLQIdcCDQLtrSOGwXO4Dm7yPkLACowZsnMR_36XH_ylnLwRdcPM3g_DGORv9s8UheK3eBDXyQf9lOKqaMpDftLtojU57A-csXe7-_emke-eXl4auoN74QQE6fgqOoAsBWt9k5HqTxWCr1AihDJS8K5mF3vyHqNaNCC0BZtFJoquWLXf7sphLAdD2lHh5_t8QL5C9vhSVA</recordid><startdate>200912</startdate><enddate>200912</enddate><creator>Houtzager, I.</creator><creator>van Wingerden, J.-W.</creator><creator>Verhaegen, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200912</creationdate><title>VARMAX-based closed-loop subspace model identification</title><author>Houtzager, I. ; van Wingerden, J.-W. ; Verhaegen, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c222t-ae9a8c001b2b5d95f34d1841d21af0fad3a1b5d222d9a7d511617025717f25a83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adaptive control</topic><topic>Autoregressive processes</topic><topic>Information retrieval</topic><topic>Least squares approximation</topic><topic>MIMO</topic><topic>Parameter estimation</topic><topic>Predictive models</topic><topic>Recursive estimation</topic><topic>State estimation</topic><topic>Wind turbines</topic><toplevel>online_resources</toplevel><creatorcontrib>Houtzager, I.</creatorcontrib><creatorcontrib>van Wingerden, J.-W.</creatorcontrib><creatorcontrib>Verhaegen, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Houtzager, I.</au><au>van Wingerden, J.-W.</au><au>Verhaegen, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>VARMAX-based closed-loop subspace model identification</atitle><btitle>Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference</btitle><stitle>CDC</stitle><date>2009-12</date><risdate>2009</risdate><spage>3370</spage><epage>3375</epage><pages>3370-3375</pages><issn>0191-2216</issn><isbn>9781424438716</isbn><isbn>1424438713</isbn><eisbn>9781424438723</eisbn><eisbn>1424438721</eisbn><abstract>In this paper a predictor-based subspace model identification method is presented that relaxes the requirement that the past window has to be large for asymptotical consistent estimates. By utilizing a VARMAX model, a finite description of the input-output relation is formulated. An extended least squares recursion is used to estimate the Markov parameters in the VARMAX model set. Using the Markov parameters the state sequence can be estimated and consequently the system matrices can be recovered. The effectiveness of the proposed method in comparison with an existing method is emphasized with a simulation study on a wind turbine model operating in closed loop.</abstract><pub>IEEE</pub><doi>10.1109/CDC.2009.5400695</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0191-2216 |
| ispartof | Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009, p.3370-3375 |
| issn | 0191-2216 |
| language | eng |
| recordid | cdi_ieee_primary_5400695 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive control Autoregressive processes Information retrieval Least squares approximation MIMO Parameter estimation Predictive models Recursive estimation State estimation Wind turbines |
| title | VARMAX-based closed-loop subspace model identification |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T14%3A39%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=VARMAX-based%20closed-loop%20subspace%20model%20identification&rft.btitle=Proceedings%20of%20the%2048h%20IEEE%20Conference%20on%20Decision%20and%20Control%20(CDC)%20held%20jointly%20with%202009%2028th%20Chinese%20Control%20Conference&rft.au=Houtzager,%20I.&rft.date=2009-12&rft.spage=3370&rft.epage=3375&rft.pages=3370-3375&rft.issn=0191-2216&rft.isbn=9781424438716&rft.isbn_list=1424438713&rft_id=info:doi/10.1109/CDC.2009.5400695&rft_dat=%3Cieee_6IE%3E5400695%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424438723&rft.eisbn_list=1424438721&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5400695&rfr_iscdi=true |