Data-Driven Iterative Learning Predictive Control for Power Converters
This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on power electronics 2022-12, Vol.37 (12), p.14028-14033 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 14033 |
|---|---|
| container_issue | 12 |
| container_start_page | 14028 |
| container_title | IEEE transactions on power electronics |
| container_volume | 37 |
| creator | Wu, Wenjie Qiu, Lin Liu, Xing Guo, Feng Rodriguez, Jose Ma, Jien Fang, Youtong |
| description | This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method. |
| doi_str_mv | 10.1109/TPEL.2022.3194518 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9844292</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9844292</ieee_id><sourcerecordid>2709158091</sourcerecordid><originalsourceid>FETCH-LOGICAL-c223t-10720993bc846fb81f619e0df569e57f3c4c5346aa338618e1d845c31cf601503</originalsourceid><addsrcrecordid>eNo9UMtOwzAQtBBIlMIHIC6ROKfs-pHaR9QHVIpED-Vsue4apSpJcdIi_h6HVlx2R6OZ3dEwdo8wQgTztFrOyhEHzkcCjVSoL9ggAcwBYXzJBqC1yrUx4prdtO0WAKUCHLD51HUun8bqSHW26Ci6LsGsJBfrqv7IlpE2lf_jJk3dxWaXhSZmy-abYs8cKSZTe8uugtu1dHfeQ_Y-n60mr3n59rKYPJe551x0ecrCIYVYey2LsNYYCjQEm6AKQ2ochJdeCVk4J4QuUBNutFReoA8FoAIxZI-nu_vYfB2o7ey2OcQ6vbR8DAaVTiOp8KTysWnbSMHuY_Xp4o9FsH1dtq_L9nXZc13J83DyVET0rzdaSm64-AXCxWTf</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2709158091</pqid></control><display><type>article</type><title>Data-Driven Iterative Learning Predictive Control for Power Converters</title><source>IEEE Electronic Library (IEL)</source><creator>Wu, Wenjie ; Qiu, Lin ; Liu, Xing ; Guo, Feng ; Rodriguez, Jose ; Ma, Jien ; Fang, Youtong</creator><creatorcontrib>Wu, Wenjie ; Qiu, Lin ; Liu, Xing ; Guo, Feng ; Rodriguez, Jose ; Ma, Jien ; Fang, Youtong</creatorcontrib><description>This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.</description><identifier>ISSN: 0885-8993</identifier><identifier>EISSN: 1941-0107</identifier><identifier>DOI: 10.1109/TPEL.2022.3194518</identifier><identifier>CODEN: ITPEE8</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptation models ; Adaptive control ; Data models ; Data-driven control ; Error analysis ; finite control-set model predictive control (FCS-MPC) ; iterative learning control ; Iterative methods ; Learning ; Mathematical models ; Optimal control ; Parameters ; Perturbation ; Power converters ; Predictive control ; Predictive models ; Robustness ; Switches ; Tracking errors ; Voltage control</subject><ispartof>IEEE transactions on power electronics, 2022-12, Vol.37 (12), p.14028-14033</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c223t-10720993bc846fb81f619e0df569e57f3c4c5346aa338618e1d845c31cf601503</citedby><cites>FETCH-LOGICAL-c223t-10720993bc846fb81f619e0df569e57f3c4c5346aa338618e1d845c31cf601503</cites><orcidid>0000-0001-9685-2862 ; 0000-0001-6970-3634 ; 0000-0002-8521-4184 ; 0000-0001-9173-7099 ; 0000-0003-1236-2191 ; 0000-0002-1410-4121</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9844292$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9844292$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wu, Wenjie</creatorcontrib><creatorcontrib>Qiu, Lin</creatorcontrib><creatorcontrib>Liu, Xing</creatorcontrib><creatorcontrib>Guo, Feng</creatorcontrib><creatorcontrib>Rodriguez, Jose</creatorcontrib><creatorcontrib>Ma, Jien</creatorcontrib><creatorcontrib>Fang, Youtong</creatorcontrib><title>Data-Driven Iterative Learning Predictive Control for Power Converters</title><title>IEEE transactions on power electronics</title><addtitle>TPEL</addtitle><description>This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.</description><subject>Adaptation models</subject><subject>Adaptive control</subject><subject>Data models</subject><subject>Data-driven control</subject><subject>Error analysis</subject><subject>finite control-set model predictive control (FCS-MPC)</subject><subject>iterative learning control</subject><subject>Iterative methods</subject><subject>Learning</subject><subject>Mathematical models</subject><subject>Optimal control</subject><subject>Parameters</subject><subject>Perturbation</subject><subject>Power converters</subject><subject>Predictive control</subject><subject>Predictive models</subject><subject>Robustness</subject><subject>Switches</subject><subject>Tracking errors</subject><subject>Voltage control</subject><issn>0885-8993</issn><issn>1941-0107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9UMtOwzAQtBBIlMIHIC6ROKfs-pHaR9QHVIpED-Vsue4apSpJcdIi_h6HVlx2R6OZ3dEwdo8wQgTztFrOyhEHzkcCjVSoL9ggAcwBYXzJBqC1yrUx4prdtO0WAKUCHLD51HUun8bqSHW26Ci6LsGsJBfrqv7IlpE2lf_jJk3dxWaXhSZmy-abYs8cKSZTe8uugtu1dHfeQ_Y-n60mr3n59rKYPJe551x0ecrCIYVYey2LsNYYCjQEm6AKQ2ochJdeCVk4J4QuUBNutFReoA8FoAIxZI-nu_vYfB2o7ey2OcQ6vbR8DAaVTiOp8KTysWnbSMHuY_Xp4o9FsH1dtq_L9nXZc13J83DyVET0rzdaSm64-AXCxWTf</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Wu, Wenjie</creator><creator>Qiu, Lin</creator><creator>Liu, Xing</creator><creator>Guo, Feng</creator><creator>Rodriguez, Jose</creator><creator>Ma, Jien</creator><creator>Fang, Youtong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-9685-2862</orcidid><orcidid>https://orcid.org/0000-0001-6970-3634</orcidid><orcidid>https://orcid.org/0000-0002-8521-4184</orcidid><orcidid>https://orcid.org/0000-0001-9173-7099</orcidid><orcidid>https://orcid.org/0000-0003-1236-2191</orcidid><orcidid>https://orcid.org/0000-0002-1410-4121</orcidid></search><sort><creationdate>20221201</creationdate><title>Data-Driven Iterative Learning Predictive Control for Power Converters</title><author>Wu, Wenjie ; Qiu, Lin ; Liu, Xing ; Guo, Feng ; Rodriguez, Jose ; Ma, Jien ; Fang, Youtong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c223t-10720993bc846fb81f619e0df569e57f3c4c5346aa338618e1d845c31cf601503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptation models</topic><topic>Adaptive control</topic><topic>Data models</topic><topic>Data-driven control</topic><topic>Error analysis</topic><topic>finite control-set model predictive control (FCS-MPC)</topic><topic>iterative learning control</topic><topic>Iterative methods</topic><topic>Learning</topic><topic>Mathematical models</topic><topic>Optimal control</topic><topic>Parameters</topic><topic>Perturbation</topic><topic>Power converters</topic><topic>Predictive control</topic><topic>Predictive models</topic><topic>Robustness</topic><topic>Switches</topic><topic>Tracking errors</topic><topic>Voltage control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Wenjie</creatorcontrib><creatorcontrib>Qiu, Lin</creatorcontrib><creatorcontrib>Liu, Xing</creatorcontrib><creatorcontrib>Guo, Feng</creatorcontrib><creatorcontrib>Rodriguez, Jose</creatorcontrib><creatorcontrib>Ma, Jien</creatorcontrib><creatorcontrib>Fang, Youtong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on power electronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wu, Wenjie</au><au>Qiu, Lin</au><au>Liu, Xing</au><au>Guo, Feng</au><au>Rodriguez, Jose</au><au>Ma, Jien</au><au>Fang, Youtong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-Driven Iterative Learning Predictive Control for Power Converters</atitle><jtitle>IEEE transactions on power electronics</jtitle><stitle>TPEL</stitle><date>2022-12-01</date><risdate>2022</risdate><volume>37</volume><issue>12</issue><spage>14028</spage><epage>14033</epage><pages>14028-14033</pages><issn>0885-8993</issn><eissn>1941-0107</eissn><coden>ITPEE8</coden><abstract>This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPEL.2022.3194518</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0001-9685-2862</orcidid><orcidid>https://orcid.org/0000-0001-6970-3634</orcidid><orcidid>https://orcid.org/0000-0002-8521-4184</orcidid><orcidid>https://orcid.org/0000-0001-9173-7099</orcidid><orcidid>https://orcid.org/0000-0003-1236-2191</orcidid><orcidid>https://orcid.org/0000-0002-1410-4121</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0885-8993 |
| ispartof | IEEE transactions on power electronics, 2022-12, Vol.37 (12), p.14028-14033 |
| issn | 0885-8993 1941-0107 |
| language | eng |
| recordid | cdi_ieee_primary_9844292 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptation models Adaptive control Data models Data-driven control Error analysis finite control-set model predictive control (FCS-MPC) iterative learning control Iterative methods Learning Mathematical models Optimal control Parameters Perturbation Power converters Predictive control Predictive models Robustness Switches Tracking errors Voltage control |
| title | Data-Driven Iterative Learning Predictive Control for Power Converters |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-19T11%3A50%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data-Driven%20Iterative%20Learning%20Predictive%20Control%20for%20Power%20Converters&rft.jtitle=IEEE%20transactions%20on%20power%20electronics&rft.au=Wu,%20Wenjie&rft.date=2022-12-01&rft.volume=37&rft.issue=12&rft.spage=14028&rft.epage=14033&rft.pages=14028-14033&rft.issn=0885-8993&rft.eissn=1941-0107&rft.coden=ITPEE8&rft_id=info:doi/10.1109/TPEL.2022.3194518&rft_dat=%3Cproquest_RIE%3E2709158091%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2709158091&rft_id=info:pmid/&rft_ieee_id=9844292&rfr_iscdi=true |