A High-Order Internal Model Based Iterative Learning Control Scheme for Nonlinear Systems With Time-Iteration-Varying Parameters
In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any conti...
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| Veröffentlicht in: | IEEE transactions on automatic control 2010-11, Vol.55 (11), p.2665-2670 |
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| container_title | IEEE transactions on automatic control |
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| creator | Yin, Chenkun Xu, Jian-Xin Hou, Zhongsheng |
| description | In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored. |
| doi_str_mv | 10.1109/TAC.2010.2069372 |
| format | Article |
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The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored.</description><identifier>ISSN: 0018-9286</identifier><identifier>EISSN: 1558-2523</identifier><identifier>DOI: 10.1109/TAC.2010.2069372</identifier><identifier>CODEN: IETAA9</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Adaptative systems ; Applied sciences ; Computer science; control theory; systems ; Control systems ; Control theory. 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(IEEE) Nov 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c354t-a70afa196de606968ab853027d1fc34815f9b6b5c0a16354ee6d4776ea3d13103</citedby><cites>FETCH-LOGICAL-c354t-a70afa196de606968ab853027d1fc34815f9b6b5c0a16354ee6d4776ea3d13103</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5555945$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5555945$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23387288$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yin, Chenkun</creatorcontrib><creatorcontrib>Xu, Jian-Xin</creatorcontrib><creatorcontrib>Hou, Zhongsheng</creatorcontrib><title>A High-Order Internal Model Based Iterative Learning Control Scheme for Nonlinear Systems With Time-Iteration-Varying Parameters</title><title>IEEE transactions on automatic control</title><addtitle>TAC</addtitle><description>In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored.</description><subject>Adaptative systems</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Control systems</subject><subject>Control theory. Systems</subject><subject>Convergence</subject><subject>Dynamical systems</subject><subject>Estimation</subject><subject>Exact sciences and technology</subject><subject>High-order internal model</subject><subject>iteration-varying</subject><subject>iterative learning control (ILC)</subject><subject>Iterative methods</subject><subject>Learning</subject><subject>Learning systems</subject><subject>Manipulators</subject><subject>Mathematical models</subject><subject>Nonlinear dynamics</subject><subject>nonlinear system</subject><subject>Nonlinear systems</subject><subject>parametric uncertainty</subject><subject>Studies</subject><subject>Trajectory</subject><subject>Uncertainty</subject><issn>0018-9286</issn><issn>1558-2523</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkc1rGzEQxUVJoY7be6EXQQjkso4-Vlrt0TFJY3Drgt32uIx3Z2OZXcmR1oHc-qdXxiaHzGWY0e89GD1CvnI24ZyVt-vpbCJYmgTTpSzEBzLiSplMKCEvyIgxbrJSGP2JXMa4S6POcz4i_6b00T5ts2VoMNC5GzA46OgP32BH7yBiQ-dpB4N9QbpACM66Jzrzbgi-o6t6iz3S1gf607vOugTQ1WscsI_0rx22dG17zM4O3mV_ILweDX5BgB7TOn4mH1voIn459zH5_XC_nj1mi-X3-Wy6yGqp8iGDgkELvNQN6nSgNrAxSjJRNLytZW64asuN3qiaAddJgaibvCg0gmy45EyOyc3Jdx_88wHjUPU21th14NAfYsWlVlwZUaiEXr1Dd_5w_JZEMcl4XkpdJIqdqDr4GAO21T7YPt2XoOoYSZUiqY6RVOdIkuT6bAyxhq4N4Gob33RCSlMIYxL37cRZRHx7VqnKXMn_WG2T5w</recordid><startdate>20101101</startdate><enddate>20101101</enddate><creator>Yin, Chenkun</creator><creator>Xu, Jian-Xin</creator><creator>Hou, Zhongsheng</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>20101101</creationdate><title>A High-Order Internal Model Based Iterative Learning Control Scheme for Nonlinear Systems With Time-Iteration-Varying Parameters</title><author>Yin, Chenkun ; Xu, Jian-Xin ; Hou, Zhongsheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c354t-a70afa196de606968ab853027d1fc34815f9b6b5c0a16354ee6d4776ea3d13103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Adaptative systems</topic><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Control systems</topic><topic>Control theory. Systems</topic><topic>Convergence</topic><topic>Dynamical systems</topic><topic>Estimation</topic><topic>Exact sciences and technology</topic><topic>High-order internal model</topic><topic>iteration-varying</topic><topic>iterative learning control (ILC)</topic><topic>Iterative methods</topic><topic>Learning</topic><topic>Learning systems</topic><topic>Manipulators</topic><topic>Mathematical models</topic><topic>Nonlinear dynamics</topic><topic>nonlinear system</topic><topic>Nonlinear systems</topic><topic>parametric uncertainty</topic><topic>Studies</topic><topic>Trajectory</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yin, Chenkun</creatorcontrib><creatorcontrib>Xu, Jian-Xin</creatorcontrib><creatorcontrib>Hou, Zhongsheng</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>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</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>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on automatic control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yin, Chenkun</au><au>Xu, Jian-Xin</au><au>Hou, Zhongsheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A High-Order Internal Model Based Iterative Learning Control Scheme for Nonlinear Systems With Time-Iteration-Varying Parameters</atitle><jtitle>IEEE transactions on automatic control</jtitle><stitle>TAC</stitle><date>2010-11-01</date><risdate>2010</risdate><volume>55</volume><issue>11</issue><spage>2665</spage><epage>2670</epage><pages>2665-2670</pages><issn>0018-9286</issn><eissn>1558-2523</eissn><coden>IETAA9</coden><abstract>In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TAC.2010.2069372</doi><tpages>6</tpages></addata></record> |
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| subjects | Adaptative systems Applied sciences Computer science control theory systems Control systems Control theory. Systems Convergence Dynamical systems Estimation Exact sciences and technology High-order internal model iteration-varying iterative learning control (ILC) Iterative methods Learning Learning systems Manipulators Mathematical models Nonlinear dynamics nonlinear system Nonlinear systems parametric uncertainty Studies Trajectory Uncertainty |
| title | A High-Order Internal Model Based Iterative Learning Control Scheme for Nonlinear Systems With Time-Iteration-Varying Parameters |
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