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Optimizing Pinned Nodes to Maximize the Convergence Rate of Multiagent Systems with Digraph Topologies

机译:优化固定节点,以最大化具有拓扑拓扑的多态系统的收敛速率

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This paper investigates how to choose pinned node set to maximize the convergence rate of multiagent systems under digraph topologies in cases of sufficiently small and large pinning strength. In the case of sufficiently small pinning strength, perturbation methods are employed to derive formulas in terms of asymptotics that indicate that the left eigenvector corresponding to eigenvalue zero of the Laplacian measures the importance of node in pinning control multiagent systems if the underlying network has a spanning tree, whereas for the network with no spanning trees, the left eigenvectors of the Laplacian matrix corresponding to eigenvalue zero can be used to select the optimal pinned node set. In the case of sufficiently large pinning strength, by the similar method, a metric based on the smallest real part of eigenvalues of the Laplacian submatrix corresponding to the unpinned nodes is used to measure the stabilizability of the pinned node set. Different algorithms that are applicable for different scenarios are develped. Several numerical simulations are given to verify theoretical results.
机译:本文研究了如何选择固定节点集,以最大限度地在充分小且大的钉扎强度的情况下最大化多层系统的变形系统的收敛速度。在循环强度足够小的钉扎强度的情况下,在渐近学方面用于衍生公式,表明,如果底层网络具有跨度,则对应于拉普拉斯的特征值零的左特征向量测量节点的重要性树,而对于没有生成树的网络,与特征值零对应的拉普拉斯矩阵的左特征向量可用于选择最佳固定节点集。在具有类似的方法的情况下,通过类似的方法,使用基于对应于未衬里节点的拉普拉斯子素的特征值的最小实位的度量来测量固定节点集的稳定性。适用于不同场景的不同算法是开发的。给出了几个数值模拟来验证理论结果。

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