Consensus via adaptation of the network structure

DeLellis, Pietro; Bernardo, Mario di; Scafuti, Francesco

doi:10.1109/cdc.2013.6759957

Cited by 2 publications

References 30 publications

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A novel fully distributed discrete-time min–max consensus seeking algorithm with high convergence speed

Rezaei,

Khanmirza

2024

IMA Journal of Mathematical Control and Information

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In this paper, we propose a novel discrete-time consensus algorithm based on the new min–max criterion for the discrete-time second-order linear agents. To boost the convergence speed, the proposed algorithm allocates three different operating modes to the network agents. Unlike most of the existing consensus algorithms in which the links of the communication graph have specific weights and these weights are required to have positive lower bounds to achieve asymptotic convergence, the communication graph in this paper has unspecific link weights. Accordingly, because of data transmission errors and faults, the weights of the communication links can even go from a positive to a negative value. Thus, the proposed algorithm can bring the agents to consensus under more general conditions. To demonstrate the efficacy of the proposed algorithm, numerical simulations are performed, and their results are compared with those of the minimum consensus algorithm, which shows a significantly faster convergence speed.

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A novel fully distributed discrete-time min–max consensus seeking algorithm with high convergence speed

Rezaei,

Khanmirza

2024

IMA Journal of Mathematical Control and Information

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Analysis of threshold models for collective actions in social networks

Garulli

Giannitrapani

Valentini

2015

2015 European Control Conference (ECC)

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In this paper, we study the asymptotic behaviors of threshold models used to describe the formation of collective actions in social networks. At each time instant, each agent of the network makes a choice between two possible actions. The decision is made on the basis of the actions chosen by its neighbors and the value of a dynamically updated threshold. The main novelty of the proposed model is the introduction of a parameter accounting for the level of self-confidence of the agents, which affects the dynamic evolution of the threshold and in turn the way the agents make their decision. The objective is to identify which are the possible limiting behaviors of the network and under which conditions each of them occurs. Three different network topologies are considered. In the case of complete graph, the asymptotic behaviors are analytically characterized, whereas for star and ring graphs an extensive numerical analysis is presented

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Consensus via adaptation of the network structure

Cited by 2 publications

References 30 publications

A novel fully distributed discrete-time min–max consensus seeking algorithm with high convergence speed

A novel fully distributed discrete-time min–max consensus seeking algorithm with high convergence speed

Analysis of threshold models for collective actions in social networks

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