Accelerated Multi-Stage Discrete Time Dynamic Average Consensus

This letter presents a novel solution for the discrete time dynamic average consensus problem. Given a set of time-varying input signals over the nodes of an undirected graph, the proposed algorithm tracks, at each node, the input signals' average. The algorithm is based on a sequence of consen...

Full description

Saved in:
Bibliographic Details
Published in:IEEE control systems letters 2023, Vol.7, p.2731-2736
Main Authors: Sebastian, Eduardo, Montijano, Eduardo, Sagues, Carlos, Franceschelli, Mauro, verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please
Format: Article
Language:English
Subjects:
Consensus
Consensus protocol
Convergence
distributed control
Eigenvalues and eigenfunctions
estimation
Filtering algorithms
Heuristic algorithms
Robustness
sensor networks
Steady-state
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c312t-f1d75db77db3b3913d0c6e3df67ef80b55f05646f154dff9eaa932f5eb09d1033
cites cdi_FETCH-LOGICAL-c312t-f1d75db77db3b3913d0c6e3df67ef80b55f05646f154dff9eaa932f5eb09d1033
container_end_page 2736
container_issue
container_start_page 2731
container_title IEEE control systems letters
container_volume 7
creator Sebastian, Eduardo
Montijano, Eduardo
Sagues, Carlos
Franceschelli, Mauro
verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please
description This letter presents a novel solution for the discrete time dynamic average consensus problem. Given a set of time-varying input signals over the nodes of an undirected graph, the proposed algorithm tracks, at each node, the input signals' average. The algorithm is based on a sequence of consensus stages combined with a second order diffusive protocol. The former overcomes the need of k-th order differences of the inputs and conservation of the network state average, while the latter overcomes the trade-off between speed and accuracy of the consensus stages by just storing the previous estimate at each node. The result is a protocol that is fast, arbitrarily accurate, and robust against input noises and initializations. The protocol is extended to an asynchronous and randomized version that follows a gossiping scheme that is robust against potential delays and packet losses. We study the convergence properties of the algorithms and validate them via simulations.
doi_str_mv 10.1109/LCSYS.2023.3289483
format article
fullrecord <record><control><sourceid>crossref_ieee_</sourceid><recordid>TN_cdi_crossref_primary_10_1109_LCSYS_2023_3289483</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10163054</ieee_id><sourcerecordid>10_1109_LCSYS_2023_3289483</sourcerecordid><originalsourceid>FETCH-LOGICAL-c312t-f1d75db77db3b3913d0c6e3df67ef80b55f05646f154dff9eaa932f5eb09d1033</originalsourceid><addsrcrecordid>eNpNkMFOg0AQhjdGE5vaFzAeeAFwdocF9mQIajXBeKAePJFld9ZggBqWmvTtpbaHnuafzHz_4WPslkPEOaj7sqg-q0iAwAhFpuIML9hCxKkMeSyTy7N8zVbefwMAz0QKQi3YQ24MdTTqiWzwtuumNqwm_UXBY-vNSBMFm7aft_2g-9YE-e_8Ol-L7eBp8Dt_w66c7jytTnPJPp6fNsVLWL6vX4u8DA1yMYWO21TaJk1tgw0qjhZMQmhdkpLLoJHSgUzixHEZW-cUaa1QOEkNKMsBccnEsdeMW-9HcvXP2PZ63Ncc6oOF-t9CfbBQnyzM0N0RaonoDOAJgozxD9ZXWSk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Accelerated Multi-Stage Discrete Time Dynamic Average Consensus</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Sebastian, Eduardo ; Montijano, Eduardo ; Sagues, Carlos ; Franceschelli, Mauro ; verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please</creator><creatorcontrib>Sebastian, Eduardo ; Montijano, Eduardo ; Sagues, Carlos ; Franceschelli, Mauro ; verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please</creatorcontrib><description>This letter presents a novel solution for the discrete time dynamic average consensus problem. Given a set of time-varying input signals over the nodes of an undirected graph, the proposed algorithm tracks, at each node, the input signals' average. The algorithm is based on a sequence of consensus stages combined with a second order diffusive protocol. The former overcomes the need of k-th order differences of the inputs and conservation of the network state average, while the latter overcomes the trade-off between speed and accuracy of the consensus stages by just storing the previous estimate at each node. The result is a protocol that is fast, arbitrarily accurate, and robust against input noises and initializations. The protocol is extended to an asynchronous and randomized version that follows a gossiping scheme that is robust against potential delays and packet losses. We study the convergence properties of the algorithms and validate them via simulations.</description><identifier>ISSN: 2475-1456</identifier><identifier>EISSN: 2475-1456</identifier><identifier>DOI: 10.1109/LCSYS.2023.3289483</identifier><identifier>CODEN: ICSLBO</identifier><language>eng</language><publisher>IEEE</publisher><subject>Consensus ; Consensus protocol ; Convergence ; distributed control ; Eigenvalues and eigenfunctions ; estimation ; Filtering algorithms ; Heuristic algorithms ; Robustness ; sensor networks ; Steady-state</subject><ispartof>IEEE control systems letters, 2023, Vol.7, p.2731-2736</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c312t-f1d75db77db3b3913d0c6e3df67ef80b55f05646f154dff9eaa932f5eb09d1033</citedby><cites>FETCH-LOGICAL-c312t-f1d75db77db3b3913d0c6e3df67ef80b55f05646f154dff9eaa932f5eb09d1033</cites><orcidid>0000-0001-9671-4056 ; 0000-0001-6522-4046 ; 0000-0001-5765-9736 ; 0000-0002-3032-954X ; 0000-0002-5176-3767</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10163054$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,786,790,4043,27956,27957,27958,55147</link.rule.ids></links><search><creatorcontrib>Sebastian, Eduardo</creatorcontrib><creatorcontrib>Montijano, Eduardo</creatorcontrib><creatorcontrib>Sagues, Carlos</creatorcontrib><creatorcontrib>Franceschelli, Mauro</creatorcontrib><creatorcontrib>verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please</creatorcontrib><title>Accelerated Multi-Stage Discrete Time Dynamic Average Consensus</title><title>IEEE control systems letters</title><addtitle>LCSYS</addtitle><description>This letter presents a novel solution for the discrete time dynamic average consensus problem. Given a set of time-varying input signals over the nodes of an undirected graph, the proposed algorithm tracks, at each node, the input signals' average. The algorithm is based on a sequence of consensus stages combined with a second order diffusive protocol. The former overcomes the need of k-th order differences of the inputs and conservation of the network state average, while the latter overcomes the trade-off between speed and accuracy of the consensus stages by just storing the previous estimate at each node. The result is a protocol that is fast, arbitrarily accurate, and robust against input noises and initializations. The protocol is extended to an asynchronous and randomized version that follows a gossiping scheme that is robust against potential delays and packet losses. We study the convergence properties of the algorithms and validate them via simulations.</description><subject>Consensus</subject><subject>Consensus protocol</subject><subject>Convergence</subject><subject>distributed control</subject><subject>Eigenvalues and eigenfunctions</subject><subject>estimation</subject><subject>Filtering algorithms</subject><subject>Heuristic algorithms</subject><subject>Robustness</subject><subject>sensor networks</subject><subject>Steady-state</subject><issn>2475-1456</issn><issn>2475-1456</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpNkMFOg0AQhjdGE5vaFzAeeAFwdocF9mQIajXBeKAePJFld9ZggBqWmvTtpbaHnuafzHz_4WPslkPEOaj7sqg-q0iAwAhFpuIML9hCxKkMeSyTy7N8zVbefwMAz0QKQi3YQ24MdTTqiWzwtuumNqwm_UXBY-vNSBMFm7aft_2g-9YE-e_8Ol-L7eBp8Dt_w66c7jytTnPJPp6fNsVLWL6vX4u8DA1yMYWO21TaJk1tgw0qjhZMQmhdkpLLoJHSgUzixHEZW-cUaa1QOEkNKMsBccnEsdeMW-9HcvXP2PZ63Ncc6oOF-t9CfbBQnyzM0N0RaonoDOAJgozxD9ZXWSk</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Sebastian, Eduardo</creator><creator>Montijano, Eduardo</creator><creator>Sagues, Carlos</creator><creator>Franceschelli, Mauro</creator><creator>verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-9671-4056</orcidid><orcidid>https://orcid.org/0000-0001-6522-4046</orcidid><orcidid>https://orcid.org/0000-0001-5765-9736</orcidid><orcidid>https://orcid.org/0000-0002-3032-954X</orcidid><orcidid>https://orcid.org/0000-0002-5176-3767</orcidid></search><sort><creationdate>2023</creationdate><title>Accelerated Multi-Stage Discrete Time Dynamic Average Consensus</title><author>Sebastian, Eduardo ; Montijano, Eduardo ; Sagues, Carlos ; Franceschelli, Mauro ; verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c312t-f1d75db77db3b3913d0c6e3df67ef80b55f05646f154dff9eaa932f5eb09d1033</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Consensus</topic><topic>Consensus protocol</topic><topic>Convergence</topic><topic>distributed control</topic><topic>Eigenvalues and eigenfunctions</topic><topic>estimation</topic><topic>Filtering algorithms</topic><topic>Heuristic algorithms</topic><topic>Robustness</topic><topic>sensor networks</topic><topic>Steady-state</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sebastian, Eduardo</creatorcontrib><creatorcontrib>Montijano, Eduardo</creatorcontrib><creatorcontrib>Sagues, Carlos</creatorcontrib><creatorcontrib>Franceschelli, Mauro</creatorcontrib><creatorcontrib>verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><jtitle>IEEE control systems letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sebastian, Eduardo</au><au>Montijano, Eduardo</au><au>Sagues, Carlos</au><au>Franceschelli, Mauro</au><au>verify, Andrea Gasparri Eduardo Sebastian is listed as a Graduate. Please</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerated Multi-Stage Discrete Time Dynamic Average Consensus</atitle><jtitle>IEEE control systems letters</jtitle><stitle>LCSYS</stitle><date>2023</date><risdate>2023</risdate><volume>7</volume><spage>2731</spage><epage>2736</epage><pages>2731-2736</pages><issn>2475-1456</issn><eissn>2475-1456</eissn><coden>ICSLBO</coden><abstract>This letter presents a novel solution for the discrete time dynamic average consensus problem. Given a set of time-varying input signals over the nodes of an undirected graph, the proposed algorithm tracks, at each node, the input signals' average. The algorithm is based on a sequence of consensus stages combined with a second order diffusive protocol. The former overcomes the need of k-th order differences of the inputs and conservation of the network state average, while the latter overcomes the trade-off between speed and accuracy of the consensus stages by just storing the previous estimate at each node. The result is a protocol that is fast, arbitrarily accurate, and robust against input noises and initializations. The protocol is extended to an asynchronous and randomized version that follows a gossiping scheme that is robust against potential delays and packet losses. We study the convergence properties of the algorithms and validate them via simulations.</abstract><pub>IEEE</pub><doi>10.1109/LCSYS.2023.3289483</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0001-9671-4056</orcidid><orcidid>https://orcid.org/0000-0001-6522-4046</orcidid><orcidid>https://orcid.org/0000-0001-5765-9736</orcidid><orcidid>https://orcid.org/0000-0002-3032-954X</orcidid><orcidid>https://orcid.org/0000-0002-5176-3767</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2475-1456
ispartof IEEE control systems letters, 2023, Vol.7, p.2731-2736
issn 2475-1456
2475-1456
language eng
recordid cdi_crossref_primary_10_1109_LCSYS_2023_3289483
source IEEE Electronic Library (IEL) Journals
subjects Consensus
Consensus protocol
Convergence
distributed control
Eigenvalues and eigenfunctions
estimation
Filtering algorithms
Heuristic algorithms
Robustness
sensor networks
Steady-state
title Accelerated Multi-Stage Discrete Time Dynamic Average Consensus
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T17%3A36%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Accelerated%20Multi-Stage%20Discrete%20Time%20Dynamic%20Average%20Consensus&rft.jtitle=IEEE%20control%20systems%20letters&rft.au=Sebastian,%20Eduardo&rft.date=2023&rft.volume=7&rft.spage=2731&rft.epage=2736&rft.pages=2731-2736&rft.issn=2475-1456&rft.eissn=2475-1456&rft.coden=ICSLBO&rft_id=info:doi/10.1109/LCSYS.2023.3289483&rft_dat=%3Ccrossref_ieee_%3E10_1109_LCSYS_2023_3289483%3C/crossref_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c312t-f1d75db77db3b3913d0c6e3df67ef80b55f05646f154dff9eaa932f5eb09d1033%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10163054&rfr_iscdi=true