On finite-time and fixed-time consensus algorithms for dynamic networks switching among disconnected digraphs

0496006 - ÚTIA 2021 RIV GB eng J - Journal Article
Gómez-Gutiérrez, D. - Vázquez, C. R. - Čelikovský, Sergej - Sánchez-Torres, J. D. - Ruiz-León, J.
On finite-time and fixed-time consensus algorithms for dynamic networks switching among disconnected digraphs.
International Journal of Control. Roč. 93, č. 9 (2020), s. 2120-2134. ISSN 0020-7179. E-ISSN 1366-5820
R&D Projects: GA ČR(CZ) GA17-04682S
Institutional support: RVO:67985556
Keywords : Fixed-time consensus * Finite-time consensus * dynamical networks * multi-agent systems * multiple interacting autonomous agents * self-organizing systems
OECD category: Electrical and electronic engineering
Impact factor: 2.888, year: 2020
Method of publishing: Limited access
https://www.tandfonline.com/doi/full/10.1080/00207179.2018.1543896

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.
Permanent Link: http://hdl.handle.net/11104/0288845