Convergence speed of dynamic consensus with delay compensation

Aragues, Rosario (Universidad de Zaragoza) ; González, Antonio ; López–Nicolás, Gonzalo (Universidad de Zaragoza) ; Sagues, Carlos (Universidad de Zaragoza)

Resumen: A 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.

Idioma: Inglés

DOI: 10.1016/j.neucom.2023.127130

Año: 2024

Publicado en: Neurocomputing 570 (2024), 127130 [15 pp.]
ISSN: 0925-2312

Factor impacto JCR: 6.5 (2024)

Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 37 / 204 = 0.181 (2024) - Q1 - T1

Factor impacto SCIMAGO: 1.471 - Artificial Intelligence (Q1) - Computer Science Applications (Q1) - Cognitive Neuroscience (Q1)

Financiación: info:eu-repo/grantAgreement/EUR/AEI/TED2021-130224B-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-124137OB-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)