Fast Consensus Seeking on Networks with Antagonistic Interactions

الملخص EN

It is well known that all agents in a multiagent system can asymptotically converge to a common value based on consensus protocols.

Besides, the associated convergence rate depends on the magnitude of the smallest nonzero eigenvalue of Laplacian matrix L.

In this paper, we introduce a superposition system to superpose to the original system and study how to change the convergence rate without destroying the connectivity of undirected communication graphs.

And we find the result if the eigenvector x of eigenvalue λ has two identical entries xi=xj, then the weight and existence of the edge eij do not affect the magnitude of λ, which is the argument of this paper.

By taking advantage of the inequality of eigenvalues, conditions are derived to achieve the largest convergence rate with the largest delay margin, and, at the same time, the corresponding topology structure is characterized in detail.

In addition, a method of constructing invalid algebraic connectivity weights is proposed to keep the convergence rate unchanged.

Finally, simulations are given to demonstrate the effectiveness of the results.