Analysis of Performance for Multiagent Networks
In this article, we analyze the {H}_{\infty } performance of the first-order continuous-time multiagent consensus network and that of the corresponding sampled-data network in the presence of external disturbances. First, we build the quantitative relation between the {H}_{\infty } performance and t...
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| Veröffentlicht in: | IEEE transactions on automatic control 2024-08, Vol.69 (8), p.5125-5140 |
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| creator | Wang, Jiamin Liu, Jian Zheng, Yuanshi Xi, Jianxiang |
| description | In this article, we analyze the {H}_{\infty } performance of the first-order continuous-time multiagent consensus network and that of the corresponding sampled-data network in the presence of external disturbances. First, we build the quantitative relation between the {H}_{\infty } performance and the eigenvalues of directed graph Laplacian for the continuous-time multiagent network. Second, we establish the analytic expression of {H}_{\infty } performance for the sampled-data multiagent network, which depends not only on the eigenvalues of the Laplacian matrix but also on the sampling period. It is proved that there exists a unique optimal sampling period such that the sampled-data multiagent network obtains the optimal {H}_{\infty } performance. Furthermore, we show that the {H}_{\infty } performance of the sampled-data multiagent network is not better than that of the original continuous-time multiagent network. Finally, numerical tests are given on several well-known graphs. |
| doi_str_mv | 10.1109/TAC.2023.3342060 |
| format | Article |
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First, we build the quantitative relation between the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance and the eigenvalues of directed graph Laplacian for the continuous-time multiagent network. Second, we establish the analytic expression of <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance for the sampled-data multiagent network, which depends not only on the eigenvalues of the Laplacian matrix but also on the sampling period. It is proved that there exists a unique optimal sampling period such that the sampled-data multiagent network obtains the optimal <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance. Furthermore, we show that the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance of the sampled-data multiagent network is not better than that of the original continuous-time multiagent network. Finally, numerical tests are given on several well-known graphs.]]></description><identifier>ISSN: 0018-9286</identifier><identifier>EISSN: 1558-2523</identifier><identifier>DOI: 10.1109/TAC.2023.3342060</identifier><identifier>CODEN: IETAA9</identifier><language>eng</language><publisher>IEEE</publisher><subject><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> {H}_{\infty }</tex-math> </inline-formula> </named-content> performance ; Consensus networks ; Directed graphs ; Eigenvalues and eigenfunctions ; graph Laplacian ; Laplace equations ; Multi-agent systems ; Network topology ; optimal sampling period ; Sufficient conditions ; Uncertainty]]></subject><ispartof>IEEE transactions on automatic control, 2024-08, Vol.69 (8), p.5125-5140</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c625-6ac8c1de597719d8e29801162f982ee4ac7416ad059c89768d6bdf6c92bbdc473</cites><orcidid>0000-0001-5056-5657 ; 0000-0002-1143-2509 ; 0000-0002-7094-5726 ; 0000-0002-7472-0743</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10354485$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10354485$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Jiamin</creatorcontrib><creatorcontrib>Liu, Jian</creatorcontrib><creatorcontrib>Zheng, Yuanshi</creatorcontrib><creatorcontrib>Xi, Jianxiang</creatorcontrib><title>Analysis of Performance for Multiagent Networks</title><title>IEEE transactions on automatic control</title><addtitle>TAC</addtitle><description><![CDATA[In this article, we analyze the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance of the first-order continuous-time multiagent consensus network and that of the corresponding sampled-data network in the presence of external disturbances. First, we build the quantitative relation between the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance and the eigenvalues of directed graph Laplacian for the continuous-time multiagent network. Second, we establish the analytic expression of <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance for the sampled-data multiagent network, which depends not only on the eigenvalues of the Laplacian matrix but also on the sampling period. It is proved that there exists a unique optimal sampling period such that the sampled-data multiagent network obtains the optimal <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance. Furthermore, we show that the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance of the sampled-data multiagent network is not better than that of the original continuous-time multiagent network. Finally, numerical tests are given on several well-known graphs.]]></description><subject><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> {H}_{\infty }</tex-math> </inline-formula> </named-content> performance]]></subject><subject>Consensus networks</subject><subject>Directed graphs</subject><subject>Eigenvalues and eigenfunctions</subject><subject>graph Laplacian</subject><subject>Laplace equations</subject><subject>Multi-agent systems</subject><subject>Network topology</subject><subject>optimal sampling period</subject><subject>Sufficient conditions</subject><subject>Uncertainty</subject><issn>0018-9286</issn><issn>1558-2523</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNj01LxDAYhIMoWFfvHjz0D3Q3b76aHEvxC9aPQ-8hTd5KtdtKUpH993bZPXiaGZgZeAi5BboGoGbTVPWaUcbXnAtGFT0jGUipCyYZPycZpaALw7S6JFcpfS5RCQEZ2VSjG_apT_nU5e8Yuynu3OgxX0z-8jPMvfvAcc5fcf6d4le6JhedGxLenHRFmof7pn4qtm-Pz3W1LbxislDOaw8BpSlLMEEjM5oCKNYZzRCF86UA5QKVxmtTKh1UGzrlDWvb4EXJV4Qeb32cUorY2e_Y71zcW6D2wGsXXnvgtSfeZXJ3nPSI-K_OpRBa8j_O71C4</recordid><startdate>202408</startdate><enddate>202408</enddate><creator>Wang, Jiamin</creator><creator>Liu, Jian</creator><creator>Zheng, Yuanshi</creator><creator>Xi, Jianxiang</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-5056-5657</orcidid><orcidid>https://orcid.org/0000-0002-1143-2509</orcidid><orcidid>https://orcid.org/0000-0002-7094-5726</orcidid><orcidid>https://orcid.org/0000-0002-7472-0743</orcidid></search><sort><creationdate>202408</creationdate><title>Analysis of Performance for Multiagent Networks</title><author>Wang, Jiamin ; Liu, Jian ; Zheng, Yuanshi ; Xi, Jianxiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c625-6ac8c1de597719d8e29801162f982ee4ac7416ad059c89768d6bdf6c92bbdc473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> {H}_{\infty }</tex-math> </inline-formula> </named-content> performance]]></topic><topic>Consensus networks</topic><topic>Directed graphs</topic><topic>Eigenvalues and eigenfunctions</topic><topic>graph Laplacian</topic><topic>Laplace equations</topic><topic>Multi-agent systems</topic><topic>Network topology</topic><topic>optimal sampling period</topic><topic>Sufficient conditions</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jiamin</creatorcontrib><creatorcontrib>Liu, Jian</creatorcontrib><creatorcontrib>Zheng, Yuanshi</creatorcontrib><creatorcontrib>Xi, Jianxiang</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><jtitle>IEEE transactions on automatic control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Jiamin</au><au>Liu, Jian</au><au>Zheng, Yuanshi</au><au>Xi, Jianxiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of Performance for Multiagent Networks</atitle><jtitle>IEEE transactions on automatic control</jtitle><stitle>TAC</stitle><date>2024-08</date><risdate>2024</risdate><volume>69</volume><issue>8</issue><spage>5125</spage><epage>5140</epage><pages>5125-5140</pages><issn>0018-9286</issn><eissn>1558-2523</eissn><coden>IETAA9</coden><abstract><![CDATA[In this article, we analyze the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance of the first-order continuous-time multiagent consensus network and that of the corresponding sampled-data network in the presence of external disturbances. First, we build the quantitative relation between the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance and the eigenvalues of directed graph Laplacian for the continuous-time multiagent network. Second, we establish the analytic expression of <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance for the sampled-data multiagent network, which depends not only on the eigenvalues of the Laplacian matrix but also on the sampling period. It is proved that there exists a unique optimal sampling period such that the sampled-data multiagent network obtains the optimal <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance. Furthermore, we show that the <inline-formula><tex-math notation="LaTeX">{H}_{\infty }</tex-math></inline-formula> performance of the sampled-data multiagent network is not better than that of the original continuous-time multiagent network. Finally, numerical tests are given on several well-known graphs.]]></abstract><pub>IEEE</pub><doi>10.1109/TAC.2023.3342060</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-5056-5657</orcidid><orcidid>https://orcid.org/0000-0002-1143-2509</orcidid><orcidid>https://orcid.org/0000-0002-7094-5726</orcidid><orcidid>https://orcid.org/0000-0002-7472-0743</orcidid></addata></record> |
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| subjects | <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> {H}_{\infty }</tex-math> </inline-formula> </named-content> performance Consensus networks Directed graphs Eigenvalues and eigenfunctions graph Laplacian Laplace equations Multi-agent systems Network topology optimal sampling period Sufficient conditions Uncertainty |
| title | Analysis of Performance for Multiagent Networks |
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