Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths
In most implementations of iterative learning control (ILC) for trajectory tracking, it is usually required that the trial lengths of different iterations are uniform. However, this requirement may not always be ensured in practical applications. In this paper, a feedback-aided PD-type ILC design fo...
Gespeichert in:
Veröffentlicht in: | Transactions of the Institute of Measurement and Control 2023-07, Vol.45 (11), p.2015-2026 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 2026 |
---|---|
container_issue | 11 |
container_start_page | 2015 |
container_title | Transactions of the Institute of Measurement and Control |
container_volume | 45 |
creator | Guan, Shanglei Zhuang, Zhihe Tao, Hongfeng Chen, Yiyang Stojanovic, Vladimir Paszke, Wojciech |
description | In most implementations of iterative learning control (ILC) for trajectory tracking, it is usually required that the trial lengths of different iterations are uniform. However, this requirement may not always be ensured in practical applications. In this paper, a feedback-aided PD-type ILC design for time-varying systems with non-uniform trial lengths is proposed. Although the actual trial lengths are non-uniform, the designed update sequences provide uniform full-length signals for the update process. Meanwhile, information from the most recent valid iterations can be better used than the mechanisms that compensate with hypothesized data, such as zero. Their recursive generation also reduces the storage burden compared to search strategies. The feedback error signal can be additionally used as part of the correction term to improve the system performance compared to the traditional open-loop approaches. Under a deterministic model, the main convergence results are obtained by combining the
λ
-norm technique with the inductive analysis approach. At last, a linear numerical simulation and a nonlinear single-joint robot simulation are performed, respectively, to show that the proposed design can achieve the asymptotic tracking of the desired trajectories for time-varying systems with non-uniform trial lengths. |
doi_str_mv | 10.1177/01423312221142564 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2822650226</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.1177_01423312221142564</sage_id><sourcerecordid>2822650226</sourcerecordid><originalsourceid>FETCH-LOGICAL-c312t-e25b71c502396a7e75b21dfd48240b1daf8b0fcde8715ca17a92a7b6c8f8adbf3</originalsourceid><addsrcrecordid>eNp1kEFLAzEQhYMoWKs_wFvAc2qSzW62R6lWhYIe9Lxkk0mbus3WJK3035ulggfxNAPzvTczD6FrRieMSXlLmeBFwTjnLHdlJU7QiAkpCS2q6SkaDXMyAOfoIsY1pVSISoyQmwOYVukPopwBg1_vSTpsAbsEQSW3B9yBCt75Jda9T6HvsO0DTm4DZK_CYRjEQ0ywifjLpRX2vSc77zK0wSk41WUDv0yreInOrOoiXP3UMXqfP7zNnsji5fF5drcgOl-XCPCylUyXlBfTSkmQZcuZsUbUXNCWGWXrllptoJas1IpJNeVKtpWuba1Ma4sxujn6bkP_uYOYmnW_Cz6vbHjNeZWdeZUpdqR06GMMYJttcJv8UMNoMyTa_Ek0ayZHTVRL-HX9X_ANyI93WA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2822650226</pqid></control><display><type>article</type><title>Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths</title><source>Access via SAGE</source><creator>Guan, Shanglei ; Zhuang, Zhihe ; Tao, Hongfeng ; Chen, Yiyang ; Stojanovic, Vladimir ; Paszke, Wojciech</creator><creatorcontrib>Guan, Shanglei ; Zhuang, Zhihe ; Tao, Hongfeng ; Chen, Yiyang ; Stojanovic, Vladimir ; Paszke, Wojciech</creatorcontrib><description>In most implementations of iterative learning control (ILC) for trajectory tracking, it is usually required that the trial lengths of different iterations are uniform. However, this requirement may not always be ensured in practical applications. In this paper, a feedback-aided PD-type ILC design for time-varying systems with non-uniform trial lengths is proposed. Although the actual trial lengths are non-uniform, the designed update sequences provide uniform full-length signals for the update process. Meanwhile, information from the most recent valid iterations can be better used than the mechanisms that compensate with hypothesized data, such as zero. Their recursive generation also reduces the storage burden compared to search strategies. The feedback error signal can be additionally used as part of the correction term to improve the system performance compared to the traditional open-loop approaches. Under a deterministic model, the main convergence results are obtained by combining the
λ
-norm technique with the inductive analysis approach. At last, a linear numerical simulation and a nonlinear single-joint robot simulation are performed, respectively, to show that the proposed design can achieve the asymptotic tracking of the desired trajectories for time-varying systems with non-uniform trial lengths.</description><identifier>ISSN: 0142-3312</identifier><identifier>EISSN: 1477-0369</identifier><identifier>DOI: 10.1177/01423312221142564</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Error correction ; Error signals ; Feedback ; Iterative methods ; Learning ; Mathematical models ; Time varying control systems ; Tracking control ; Trajectory control</subject><ispartof>Transactions of the Institute of Measurement and Control, 2023-07, Vol.45 (11), p.2015-2026</ispartof><rights>The Author(s) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c312t-e25b71c502396a7e75b21dfd48240b1daf8b0fcde8715ca17a92a7b6c8f8adbf3</citedby><cites>FETCH-LOGICAL-c312t-e25b71c502396a7e75b21dfd48240b1daf8b0fcde8715ca17a92a7b6c8f8adbf3</cites><orcidid>0000-0001-9960-9040</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/01423312221142564$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/01423312221142564$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,780,784,21819,27924,27925,43621,43622</link.rule.ids></links><search><creatorcontrib>Guan, Shanglei</creatorcontrib><creatorcontrib>Zhuang, Zhihe</creatorcontrib><creatorcontrib>Tao, Hongfeng</creatorcontrib><creatorcontrib>Chen, Yiyang</creatorcontrib><creatorcontrib>Stojanovic, Vladimir</creatorcontrib><creatorcontrib>Paszke, Wojciech</creatorcontrib><title>Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths</title><title>Transactions of the Institute of Measurement and Control</title><description>In most implementations of iterative learning control (ILC) for trajectory tracking, it is usually required that the trial lengths of different iterations are uniform. However, this requirement may not always be ensured in practical applications. In this paper, a feedback-aided PD-type ILC design for time-varying systems with non-uniform trial lengths is proposed. Although the actual trial lengths are non-uniform, the designed update sequences provide uniform full-length signals for the update process. Meanwhile, information from the most recent valid iterations can be better used than the mechanisms that compensate with hypothesized data, such as zero. Their recursive generation also reduces the storage burden compared to search strategies. The feedback error signal can be additionally used as part of the correction term to improve the system performance compared to the traditional open-loop approaches. Under a deterministic model, the main convergence results are obtained by combining the
λ
-norm technique with the inductive analysis approach. At last, a linear numerical simulation and a nonlinear single-joint robot simulation are performed, respectively, to show that the proposed design can achieve the asymptotic tracking of the desired trajectories for time-varying systems with non-uniform trial lengths.</description><subject>Error correction</subject><subject>Error signals</subject><subject>Feedback</subject><subject>Iterative methods</subject><subject>Learning</subject><subject>Mathematical models</subject><subject>Time varying control systems</subject><subject>Tracking control</subject><subject>Trajectory control</subject><issn>0142-3312</issn><issn>1477-0369</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp1kEFLAzEQhYMoWKs_wFvAc2qSzW62R6lWhYIe9Lxkk0mbus3WJK3035ulggfxNAPzvTczD6FrRieMSXlLmeBFwTjnLHdlJU7QiAkpCS2q6SkaDXMyAOfoIsY1pVSISoyQmwOYVukPopwBg1_vSTpsAbsEQSW3B9yBCt75Jda9T6HvsO0DTm4DZK_CYRjEQ0ywifjLpRX2vSc77zK0wSk41WUDv0yreInOrOoiXP3UMXqfP7zNnsji5fF5drcgOl-XCPCylUyXlBfTSkmQZcuZsUbUXNCWGWXrllptoJas1IpJNeVKtpWuba1Ma4sxujn6bkP_uYOYmnW_Cz6vbHjNeZWdeZUpdqR06GMMYJttcJv8UMNoMyTa_Ek0ayZHTVRL-HX9X_ANyI93WA</recordid><startdate>202307</startdate><enddate>202307</enddate><creator>Guan, Shanglei</creator><creator>Zhuang, Zhihe</creator><creator>Tao, Hongfeng</creator><creator>Chen, Yiyang</creator><creator>Stojanovic, Vladimir</creator><creator>Paszke, Wojciech</creator><general>SAGE Publications</general><general>Sage Publications Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-9960-9040</orcidid></search><sort><creationdate>202307</creationdate><title>Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths</title><author>Guan, Shanglei ; Zhuang, Zhihe ; Tao, Hongfeng ; Chen, Yiyang ; Stojanovic, Vladimir ; Paszke, Wojciech</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c312t-e25b71c502396a7e75b21dfd48240b1daf8b0fcde8715ca17a92a7b6c8f8adbf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Error correction</topic><topic>Error signals</topic><topic>Feedback</topic><topic>Iterative methods</topic><topic>Learning</topic><topic>Mathematical models</topic><topic>Time varying control systems</topic><topic>Tracking control</topic><topic>Trajectory control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guan, Shanglei</creatorcontrib><creatorcontrib>Zhuang, Zhihe</creatorcontrib><creatorcontrib>Tao, Hongfeng</creatorcontrib><creatorcontrib>Chen, Yiyang</creatorcontrib><creatorcontrib>Stojanovic, Vladimir</creatorcontrib><creatorcontrib>Paszke, Wojciech</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Transactions of the Institute of Measurement and Control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guan, Shanglei</au><au>Zhuang, Zhihe</au><au>Tao, Hongfeng</au><au>Chen, Yiyang</au><au>Stojanovic, Vladimir</au><au>Paszke, Wojciech</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths</atitle><jtitle>Transactions of the Institute of Measurement and Control</jtitle><date>2023-07</date><risdate>2023</risdate><volume>45</volume><issue>11</issue><spage>2015</spage><epage>2026</epage><pages>2015-2026</pages><issn>0142-3312</issn><eissn>1477-0369</eissn><abstract>In most implementations of iterative learning control (ILC) for trajectory tracking, it is usually required that the trial lengths of different iterations are uniform. However, this requirement may not always be ensured in practical applications. In this paper, a feedback-aided PD-type ILC design for time-varying systems with non-uniform trial lengths is proposed. Although the actual trial lengths are non-uniform, the designed update sequences provide uniform full-length signals for the update process. Meanwhile, information from the most recent valid iterations can be better used than the mechanisms that compensate with hypothesized data, such as zero. Their recursive generation also reduces the storage burden compared to search strategies. The feedback error signal can be additionally used as part of the correction term to improve the system performance compared to the traditional open-loop approaches. Under a deterministic model, the main convergence results are obtained by combining the
λ
-norm technique with the inductive analysis approach. At last, a linear numerical simulation and a nonlinear single-joint robot simulation are performed, respectively, to show that the proposed design can achieve the asymptotic tracking of the desired trajectories for time-varying systems with non-uniform trial lengths.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><doi>10.1177/01423312221142564</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-9960-9040</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0142-3312 |
ispartof | Transactions of the Institute of Measurement and Control, 2023-07, Vol.45 (11), p.2015-2026 |
issn | 0142-3312 1477-0369 |
language | eng |
recordid | cdi_proquest_journals_2822650226 |
source | Access via SAGE |
subjects | Error correction Error signals Feedback Iterative methods Learning Mathematical models Time varying control systems Tracking control Trajectory control |
title | Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T17%3A45%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Feedback-aided%20PD-type%20iterative%20learning%20control%20for%20time-varying%20systems%20with%20non-uniform%20trial%20lengths&rft.jtitle=Transactions%20of%20the%20Institute%20of%20Measurement%20and%20Control&rft.au=Guan,%20Shanglei&rft.date=2023-07&rft.volume=45&rft.issue=11&rft.spage=2015&rft.epage=2026&rft.pages=2015-2026&rft.issn=0142-3312&rft.eissn=1477-0369&rft_id=info:doi/10.1177/01423312221142564&rft_dat=%3Cproquest_cross%3E2822650226%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2822650226&rft_id=info:pmid/&rft_sage_id=10.1177_01423312221142564&rfr_iscdi=true |