0496006 20240111141009.120181107235959.9 85057300373 000558913100009 10.1080/00207179.2018.1543896 17 s. P cav_un_epca*02567860020-7179939 (2020)2120-2134Taylor & Francis Fixed-time consensus Finite-time consensus dynamical networks multi-agent systems multiple interacting autonomous agents self-organizing systems cav_un_auth*0343234 Gómez-Gutiérrez D. MX cav_un_auth*0367151 Vázquez C. R. MX cav_un_auth*0101074 Čelikovský Sergej UTIA-B Teorie řízení Department of Control Theory TŘ TR Department of Control Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0367129 Sánchez-Torres J. D. MX cav_un_auth*0213231 Ruiz-León J. MX 664 kB https://www.tandfonline.com/doi/full/10.1080/00207179.2018.1543896 GA17-04682S GA ČR CZ cav_un_auth*0347203 The aim of this paper is to analyze a class of consensus algorithms with finite-time or fixed-time convergence for dynamic networks formed by agents with first-order dynamics. In particular, in the analyzed class a single evaluation of a nonlinear function of the consensus error is performed per each node. The classical assumption of switching among connected graphs is dropped here, allowing to represent failures and intermittent communications between agents. Thus, conditions to guarantee finite and fixed-time convergence, even while switching among disconnected graphs, are provided. Moreover, the algorithms of the considered class are shown to be computationally simpler than previously proposed finite-time consensus algorithms for dynamic networks, which is an important feature in scenarios with computationally limited nodes and energy efficiency requirements such as in sensor networks. The performance of the considered consensus algorithms is illustrated through simulations, comparing it to existing approaches for dynamic networks with finite-time and fixed-time convergence. It is shown that the settling time of the considered algorithms grows slower when the number of nodes increases than with other consensus algorithms for dynamic networks. WOS BC 20000 20200 20201 2021 5 RVO:67985556 http://hdl.handle.net/11104/0288845 cav_un_auth*0367152 Multi-agent autonomous systems lab, Intel Labs, Intel Tecnología de México MX cav_un_auth*0367153 Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias MX cav_un_auth*0367154 Research Laboratory on Optimal Design, Devices and Advanced Materials -OPTIMA-, Department of Mathematics and Physics, ITESO MX cav_un_auth*0343241 CINVESTAV, Unidad Guadalajara MX S 2 Article Automation Control Systems C AUTOMATION&CONTROLSYSTEMS 2.759 1.02 9.5 0.00824 0.763 9505 131 0.793 33.37 2.57 1810 Q2 2.533 488 Q3 41.816 50 Q3 Q2 0.7 Q2 50 2020 85057300373 SCOPUS PUBMED 000558913100009 WOS 664 kB cav_un_epca*0256786 International Journal of Control 0020-7179 1366-5820 Roč. 93 č. 9 2020 2120 2134 Taylor & Francis