Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics
Dear editor, Iterative learning control（ILC）has a wellestablished research history,as shown in[1,2].By generating a correct control signal from the previous control execution,it can achieve perfect tracking performance on a finite time interval.In the process of ILC design,the D-type[1]and P-type le...
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| Veröffentlicht in: | Science China. Information sciences 2017-07, Vol.60 (7), p.264-266, Article 079202 |
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| container_title | Science China. Information sciences |
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| creator | Fu, Qin Gu, Panpan Li, Xiangdong Wu, Jianrong |
| description | Dear editor,
Iterative learning control（ILC）has a wellestablished research history,as shown in[1,2].By generating a correct control signal from the previous control execution,it can achieve perfect tracking performance on a finite time interval.In the process of ILC design,the D-type[1]and P-type learning schemes[2]are often used to obtain good tracking performance and are applied,respectively,to irregular systems（without direct transmission from inputs to outputs）[1]and regular systems（with direct transmission from inputs to outputs）[2]. |
| doi_str_mv | 10.1007/s11432-016-0341-7 |
| format | Article |
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Iterative learning control（ILC）has a wellestablished research history,as shown in[1,2].By generating a correct control signal from the previous control execution,it can achieve perfect tracking performance on a finite time interval.In the process of ILC design,the D-type[1]and P-type learning schemes[2]are often used to obtain good tracking performance and are applied,respectively,to irregular systems（without direct transmission from inputs to outputs）[1]and regular systems（with direct transmission from inputs to outputs）[2].</description><identifier>ISSN: 1674-733X</identifier><identifier>EISSN: 1869-1919</identifier><identifier>DOI: 10.1007/s11432-016-0341-7</identifier><language>eng</language><publisher>Beijing: Science China Press</publisher><subject>Computer Science ; Information Systems and Communication Service ; Letter ; Multiagent systems ; 一致性 ; 多agent系统 ; 线性 ; 迭代学习控制</subject><ispartof>Science China. Information sciences, 2017-07, Vol.60 (7), p.264-266, Article 079202</ispartof><rights>Science China Press and Springer-Verlag Berlin Heidelberg 2017</rights><rights>Science China Press and Springer-Verlag Berlin Heidelberg 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c343t-b64299194f8693e519773a4debbd7a021d7181d8cc8c6d743a91464f6816e7053</citedby><cites>FETCH-LOGICAL-c343t-b64299194f8693e519773a4debbd7a021d7181d8cc8c6d743a91464f6816e7053</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/84009A/84009A.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11432-016-0341-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918628058?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Fu, Qin</creatorcontrib><creatorcontrib>Gu, Panpan</creatorcontrib><creatorcontrib>Li, Xiangdong</creatorcontrib><creatorcontrib>Wu, Jianrong</creatorcontrib><title>Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics</title><title>Science China. Information sciences</title><addtitle>Sci. China Inf. Sci</addtitle><addtitle>SCIENCE CHINA Information Sciences</addtitle><description>Dear editor,
Iterative learning control（ILC）has a wellestablished research history,as shown in[1,2].By generating a correct control signal from the previous control execution,it can achieve perfect tracking performance on a finite time interval.In the process of ILC design,the D-type[1]and P-type learning schemes[2]are often used to obtain good tracking performance and are applied,respectively,to irregular systems（without direct transmission from inputs to outputs）[1]and regular systems（with direct transmission from inputs to outputs）[2].</description><subject>Computer Science</subject><subject>Information Systems and Communication Service</subject><subject>Letter</subject><subject>Multiagent systems</subject><subject>一致性</subject><subject>多agent系统</subject><subject>线性</subject><subject>迭代学习控制</subject><issn>1674-733X</issn><issn>1869-1919</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1PwyAYxxujicvcB_BG9IzyAINyNIsvS5Z40cQbYZR2XTq6AdXs28tSoze5QMj_Lb-iuAZyB4TI-wjAGcUEBCaMA5ZnxQRKoTAoUOf5LSTHkrGPy2IW45bkwxihspwUbplcMKn9dKhzJvjWN8j2PoW-Q2a_D72xG1T34fQZnY9DRH2NdkOXWmwa5xOKx5jcLqKvNm1QcM3QmYC61uc0VB292bU2XhUXtemim_3c0-L96fFt8YJXr8_LxcMKW8ZZwmvBqcqTeZ3HMzcHJSUzvHLrdSUNoVBJKKEqrS2tqCRnRgEXvBYlCCfJnE2L2zE3Dz8MLia97Yfgc6WmKhOhJZmXWQWjyoY-xuBqvQ_tzoSjBqJPQPUIVGeg-gRUy-yhoydmrW9c-Ev-z3TzU7TpfXPIvt8mISlXlFJg32uBhF0</recordid><startdate>20170701</startdate><enddate>20170701</enddate><creator>Fu, Qin</creator><creator>Gu, Panpan</creator><creator>Li, Xiangdong</creator><creator>Wu, Jianrong</creator><general>Science China Press</general><general>Springer Nature B.V</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20170701</creationdate><title>Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics</title><author>Fu, Qin ; Gu, Panpan ; Li, Xiangdong ; Wu, Jianrong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c343t-b64299194f8693e519773a4debbd7a021d7181d8cc8c6d743a91464f6816e7053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computer Science</topic><topic>Information Systems and Communication Service</topic><topic>Letter</topic><topic>Multiagent systems</topic><topic>一致性</topic><topic>多agent系统</topic><topic>线性</topic><topic>迭代学习控制</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fu, Qin</creatorcontrib><creatorcontrib>Gu, Panpan</creatorcontrib><creatorcontrib>Li, Xiangdong</creatorcontrib><creatorcontrib>Wu, Jianrong</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Science China. Information sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fu, Qin</au><au>Gu, Panpan</au><au>Li, Xiangdong</au><au>Wu, Jianrong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics</atitle><jtitle>Science China. Information sciences</jtitle><stitle>Sci. China Inf. Sci</stitle><addtitle>SCIENCE CHINA Information Sciences</addtitle><date>2017-07-01</date><risdate>2017</risdate><volume>60</volume><issue>7</issue><spage>264</spage><epage>266</epage><pages>264-266</pages><artnum>079202</artnum><issn>1674-733X</issn><eissn>1869-1919</eissn><abstract>Dear editor,
Iterative learning control（ILC）has a wellestablished research history,as shown in[1,2].By generating a correct control signal from the previous control execution,it can achieve perfect tracking performance on a finite time interval.In the process of ILC design,the D-type[1]and P-type learning schemes[2]are often used to obtain good tracking performance and are applied,respectively,to irregular systems（without direct transmission from inputs to outputs）[1]and regular systems（with direct transmission from inputs to outputs）[2].</abstract><cop>Beijing</cop><pub>Science China Press</pub><doi>10.1007/s11432-016-0341-7</doi><tpages>3</tpages></addata></record> |
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| subjects | Computer Science Information Systems and Communication Service Letter Multiagent systems 一致性 多agent系统 线性 迭代学习控制 |
| title | Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics |
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