Abstract
We investigate a novel nonlinear consensus from the extreme points of doubly stochastic quadratic operators (EDSQO), based on majorization theory and Markov chains for time-varying multi-agent distributed systems. We describe a dynamic system that has a local interaction network among agents. EDSQO has been applied for distributed agent systems, on a finite dimensional stochastic matrix. We prove that multi-agent systems converge at a center (common value) via the extreme waited value of doubly stochastic quadratic operators (DSQO), which are only 1 or 0 or 1/2 1 2 if the exchanges of each agent member has no selfish communication. Applying this rule means that the consensus is nonlinear and low-complexity computational for fast time convergence. The investigated nonlinear model of EDSQO follows the structure of the DeGroot linear (DGL) consensus model. However, EDSQO is nonlinear and faster convergent than the DGL model and is of lower complexity than DSQO and cubic stochastic quadratic operators (CSQO). The simulation result and theoretical proof are illustrated.
Highlights
A Nonlinear Convergence ConsensusRawad Abdulghafor 1, * , Sultan Almotairi 2, * , Hamad Almohamedh 3 , Badr Almutairi 4 , Abdullah Bajahzar 5 and Sulaiman Sulmi Almutairi 6
Over the recent years, multi-agent systems have attracted wide-spread research attention, focusing on distributed system problems of autonomous agent groups
A new nonlinear class protocol of extreme points of doubly stochastic quadratic operators (EDSQO) for multi-agent systems has been proposed in this paper
Summary
Rawad Abdulghafor 1, * , Sultan Almotairi 2, * , Hamad Almohamedh 3 , Badr Almutairi 4 , Abdullah Bajahzar 5 and Sulaiman Sulmi Almutairi 6.
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