000148184 001__ 148184 000148184 005__ 20250923084409.0 000148184 0247_ $$2doi$$a10.1016/j.neucom.2023.127130 000148184 0248_ $$2sideral$$a135892 000148184 037__ $$aART-2024-135892 000148184 041__ $$aeng 000148184 100__ $$0(orcid)0000-0001-9458-6257$$aAragues, Rosario$$uUniversidad de Zaragoza 000148184 245__ $$aConvergence speed of dynamic consensus with delay compensation 000148184 260__ $$c2024 000148184 5060_ $$aAccess copy available to the general public$$fUnrestricted 000148184 5203_ $$aA well-known drawback in distributed average consensus of multi-agent systems is that the exchanged information is usually delayed due to the time elapsed during the data transmission process. Using classical dynamic average consensus, delays may lead to poor performance or even instability. In this paper, we propose a novel dynamic consensus method that counteracts the negative effects of delays by means of delay compensation techniques. The interest of our dynamic consensus method with delay compensation is that it converges under mild conditions on graph connectivity and bounded reference signals, no matter how large the delays are, as long as delays are fixed and known. We also provide a formal characterization of the convergence speed of our method. Additionally, our results apply to fixed directed strongly connected, and undirected topologies. 000148184 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-130224B-I00$$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-124137OB-I00 000148184 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000148184 590__ $$a6.5$$b2024 000148184 592__ $$a1.471$$b2024 000148184 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b37 / 204 = 0.181$$c2024$$dQ1$$eT1 000148184 593__ $$aArtificial Intelligence$$c2024$$dQ1 000148184 593__ $$aComputer Science Applications$$c2024$$dQ1 000148184 593__ $$aCognitive Neuroscience$$c2024$$dQ1 000148184 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000148184 700__ $$aGonzález, Antonio 000148184 700__ $$0(orcid)0000-0001-9347-5969$$aLópez–Nicolás, Gonzalo$$uUniversidad de Zaragoza 000148184 700__ $$0(orcid)0000-0002-3032-954X$$aSagues, Carlos$$uUniversidad de Zaragoza 000148184 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000148184 773__ $$g570 (2024), 127130 [15 pp.]$$pNeurocomputing$$tNeurocomputing$$x0925-2312 000148184 8564_ $$s4916070$$uhttps://zaguan.unizar.es/record/148184/files/texto_completo.pdf$$yVersión publicada 000148184 8564_ $$s2892112$$uhttps://zaguan.unizar.es/record/148184/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000148184 909CO $$ooai:zaguan.unizar.es:148184$$particulos$$pdriver 000148184 951__ $$a2025-09-22-14:29:23 000148184 980__ $$aARTICLE