Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach
This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on cybernetics 2019-01, Vol.49 (1), p.122-132 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 132 |
|---|---|
| container_issue | 1 |
| container_start_page | 122 |
| container_title | IEEE transactions on cybernetics |
| container_volume | 49 |
| creator | Ning, Boda Han, Qing-Long Zuo, Zongyu |
| description | This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results. |
| doi_str_mv | 10.1109/TCYB.2017.2766762 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2158896242</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8100702</ieee_id><sourcerecordid>2068346053</sourcerecordid><originalsourceid>FETCH-LOGICAL-c349t-578f8888a65d5628508d6e456664546c3aa1c596147ebd8d3f14f40ca246a3d43</originalsourceid><addsrcrecordid>eNpdkE1L7DAUhoMoKuoPEEEKbtx0zOdp6m4c9V5BceG4EISQaU41Mm3HJgW9v_5mmHEWnk1CzvOeHB5CjhkdMUbLi-nk5WrEKStGvAAogG-Rfc5A55wXantzh2KPHIXwQVPp9FTqXbLHy7KkTIt98nrtQ-z9bIjossdF9I3_Z6Pv2qzu-uxhmEdv37CN2dN3iNiEy2zcZjfuDfMrG1Lk1n-hy6e-wWzStQHbMIRsvFj0na3eD8lObecBj9bnAXm-vZlO_ub3j3_uJuP7vBKyjLkqdK1TWVBOAdeKagcoFQBIJaES1rJKlcBkgTOnnaiZrCWtLJdghZPigJyv5qZvPwcM0TQ-VDif2xa7IRhOQQsJVImEnv1CP7qhb9N2hjOldQlc8kSxFVX1XQg91mbR-8b234ZRs7RvlvbN0r5Z20-Z0_XkYdag2yR-XCfgZAV4RNy0NaO0oFz8B0hShtA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2158896242</pqid></control><display><type>article</type><title>Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach</title><source>IEEE Electronic Library (IEL)</source><creator>Ning, Boda ; Han, Qing-Long ; Zuo, Zongyu</creator><creatorcontrib>Ning, Boda ; Han, Qing-Long ; Zuo, Zongyu</creatorcontrib><description>This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.</description><identifier>ISSN: 2168-2267</identifier><identifier>EISSN: 2168-2275</identifier><identifier>DOI: 10.1109/TCYB.2017.2766762</identifier><identifier>PMID: 29990183</identifier><identifier>CODEN: ITCEB8</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Batteries ; Convergence ; Convex functions ; Cost function ; Dependence ; Distributed optimization ; Economic models ; fixed-time consensus ; Hessian matrices ; Invariants ; Multi-agent systems ; Multiagent systems ; Optimization ; Protocols ; Rendezvous ; Switches ; Unknown environments ; Upper bounds</subject><ispartof>IEEE transactions on cybernetics, 2019-01, Vol.49 (1), p.122-132</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-578f8888a65d5628508d6e456664546c3aa1c596147ebd8d3f14f40ca246a3d43</citedby><cites>FETCH-LOGICAL-c349t-578f8888a65d5628508d6e456664546c3aa1c596147ebd8d3f14f40ca246a3d43</cites><orcidid>0000-0002-7207-0716 ; 0000-0003-3444-9538 ; 0000-0002-7813-4872</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8100702$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8100702$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29990183$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ning, Boda</creatorcontrib><creatorcontrib>Han, Qing-Long</creatorcontrib><creatorcontrib>Zuo, Zongyu</creatorcontrib><title>Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach</title><title>IEEE transactions on cybernetics</title><addtitle>TCYB</addtitle><addtitle>IEEE Trans Cybern</addtitle><description>This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.</description><subject>Batteries</subject><subject>Convergence</subject><subject>Convex functions</subject><subject>Cost function</subject><subject>Dependence</subject><subject>Distributed optimization</subject><subject>Economic models</subject><subject>fixed-time consensus</subject><subject>Hessian matrices</subject><subject>Invariants</subject><subject>Multi-agent systems</subject><subject>Multiagent systems</subject><subject>Optimization</subject><subject>Protocols</subject><subject>Rendezvous</subject><subject>Switches</subject><subject>Unknown environments</subject><subject>Upper bounds</subject><issn>2168-2267</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1L7DAUhoMoKuoPEEEKbtx0zOdp6m4c9V5BceG4EISQaU41Mm3HJgW9v_5mmHEWnk1CzvOeHB5CjhkdMUbLi-nk5WrEKStGvAAogG-Rfc5A55wXantzh2KPHIXwQVPp9FTqXbLHy7KkTIt98nrtQ-z9bIjossdF9I3_Z6Pv2qzu-uxhmEdv37CN2dN3iNiEy2zcZjfuDfMrG1Lk1n-hy6e-wWzStQHbMIRsvFj0na3eD8lObecBj9bnAXm-vZlO_ub3j3_uJuP7vBKyjLkqdK1TWVBOAdeKagcoFQBIJaES1rJKlcBkgTOnnaiZrCWtLJdghZPigJyv5qZvPwcM0TQ-VDif2xa7IRhOQQsJVImEnv1CP7qhb9N2hjOldQlc8kSxFVX1XQg91mbR-8b234ZRs7RvlvbN0r5Z20-Z0_XkYdag2yR-XCfgZAV4RNy0NaO0oFz8B0hShtA</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>Ning, Boda</creator><creator>Han, Qing-Long</creator><creator>Zuo, Zongyu</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>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7207-0716</orcidid><orcidid>https://orcid.org/0000-0003-3444-9538</orcidid><orcidid>https://orcid.org/0000-0002-7813-4872</orcidid></search><sort><creationdate>201901</creationdate><title>Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach</title><author>Ning, Boda ; Han, Qing-Long ; Zuo, Zongyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-578f8888a65d5628508d6e456664546c3aa1c596147ebd8d3f14f40ca246a3d43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Batteries</topic><topic>Convergence</topic><topic>Convex functions</topic><topic>Cost function</topic><topic>Dependence</topic><topic>Distributed optimization</topic><topic>Economic models</topic><topic>fixed-time consensus</topic><topic>Hessian matrices</topic><topic>Invariants</topic><topic>Multi-agent systems</topic><topic>Multiagent systems</topic><topic>Optimization</topic><topic>Protocols</topic><topic>Rendezvous</topic><topic>Switches</topic><topic>Unknown environments</topic><topic>Upper bounds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ning, Boda</creatorcontrib><creatorcontrib>Han, Qing-Long</creatorcontrib><creatorcontrib>Zuo, Zongyu</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>PubMed</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>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace 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>MEDLINE - Academic</collection><jtitle>IEEE transactions on cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ning, Boda</au><au>Han, Qing-Long</au><au>Zuo, Zongyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TCYB</stitle><addtitle>IEEE Trans Cybern</addtitle><date>2019-01</date><risdate>2019</risdate><volume>49</volume><issue>1</issue><spage>122</spage><epage>132</epage><pages>122-132</pages><issn>2168-2267</issn><eissn>2168-2275</eissn><coden>ITCEB8</coden><abstract>This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>29990183</pmid><doi>10.1109/TCYB.2017.2766762</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-7207-0716</orcidid><orcidid>https://orcid.org/0000-0003-3444-9538</orcidid><orcidid>https://orcid.org/0000-0002-7813-4872</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2168-2267 |
| ispartof | IEEE transactions on cybernetics, 2019-01, Vol.49 (1), p.122-132 |
| issn | 2168-2267 2168-2275 |
| language | eng |
| recordid | cdi_proquest_journals_2158896242 |
| source | IEEE Electronic Library (IEL) |
| subjects | Batteries Convergence Convex functions Cost function Dependence Distributed optimization Economic models fixed-time consensus Hessian matrices Invariants Multi-agent systems Multiagent systems Optimization Protocols Rendezvous Switches Unknown environments Upper bounds |
| title | Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T14%3A24%3A42IST&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=Distributed%20Optimization%20for%20Multiagent%20Systems:%20An%20Edge-Based%20Fixed-Time%20Consensus%20Approach&rft.jtitle=IEEE%20transactions%20on%20cybernetics&rft.au=Ning,%20Boda&rft.date=2019-01&rft.volume=49&rft.issue=1&rft.spage=122&rft.epage=132&rft.pages=122-132&rft.issn=2168-2267&rft.eissn=2168-2275&rft.coden=ITCEB8&rft_id=info:doi/10.1109/TCYB.2017.2766762&rft_dat=%3Cproquest_RIE%3E2068346053%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=2158896242&rft_id=info:pmid/29990183&rft_ieee_id=8100702&rfr_iscdi=true |