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arXiv 2303.14733eess.SYcs.SYmath.OC
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随机矩阵加权一致性

Randomized Matrix Weighted Consensus

Nhat-Minh Le-Phan, Minh Hoang Trinh, Phuoc Doan Nguyen

AI总结:

本文提出适用于无领导者和领导者—跟随者拓扑的随机矩阵加权一致性算法,证明期望一致性并给出 ε-收敛时间上界,且成功应用于方位网络定位与编队控制。

摘要:

本文针对无领导者和领导者—跟随者两种拓扑,提出了随机流言型矩阵加权一致性算法。首先,我们引入期望矩阵加权网络的概念,该概念从概率意义上刻画任意两个智能体之间的多维交互。在关于期望矩阵权重分布和更新步长上界的若干温和假设下,所提出的异步成对更新算法驱动网络在期望意义下达成一致性。随后推导了该算法 ε-收敛时间的上界。此外,所提算法被应用于基于方位的网络定位和编队控制问题。若干数值算例支持了这些理论结果。

英文摘要:

In this paper, randomized gossip-type matrix-weighted consensus algorithms are proposed for both leaderless and leader-follower topologies. First, we introduce the notion of expected matrix-weighted network, which captures the multi-dimensional interactions between any two agents in a probabilistic sense. Under some mild assumptions on the distribution of the expected matrix weights and the upper bound of the updating step size, the proposed asynchronous pairwise update algorithms drive the network to achieve a consensus in expectation. An upper bound of the $ε$-convergence time of the algorithm is then derived. Furthermore, the proposed algorithms are applied to the bearing-based network localization and formation control problems. The theoretical results are supported by several numerical examples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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