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Xerrada amb Daniele Astolfi: "New results in synchronization of nonlinear multi-agent Systems"

El dijous 26 d'octubre a les 12 hores, a la Sala de presentacions 28.8 de l'ETSEIB, l'investigador Daniele Astolfi realitzarà la xerrada "New results in synchronization of nonlinear multi-agent Systems"

26/10/2023 des de/d' 12:00"
Sala de presentacions 28.8
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  • Daniele Astolfi received the B.S. and M.S. degrees in automation engineering from the University of Bologna, Italy, in 2009 and 2012, respectively. He obtained a joint Ph.D. degree in Control Theory from the University of Bologna, Italy, and from Mines ParisTech, France, in 2016. In 2016 and 2017, he has been a Research Assistant at the University of Lorraine (CRAN), Nancy, France. Since 2018, he is a CNRS Researcher at LAGEPP, Lyon, France. His research interests include observer design, feedback stabilization and output regulation for nonlinear systems, networked control systems, hybrid systems, and multi-agent systems. Since 2018 he serves as an associate editor of the IFAC journal Automatica and since 2023 for European Journal of Control. He was a recipient of the 2016 Best Italian Ph.D. Thesis Award in Automatica given by Società Italiana Docenti e Ricercatori in Automatica (SIDRA, Italian Society of Professors and Researchers in Automation Engineering) and nominated as best student paper award at ECC16 and best conference paper award at NOLCOS 2019.
  • https://scholar.google.com/citations?hl=en&user=CglFtqQAAAAJ
  • Abstract: In this talk we present some recent results on the problem of state synchronization of identical multi-agent systems described by nonlinear dynamics. The proposed sufficient and necessary conditions are studied within the framework of contraction and a metric-based analysis.
  • General sufficient conditions based on a killing vector condition will be given to address the problem for systems described by input-affine nonlinear models, and some more constructive approaches will be briefly discussed.
  • This work is part of the project MASHED (TED2021-129927B-I00), funded by MCIN/AEI/10.13039/501100011033 and by the European Union Next GenerationEU/PRTR.