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arXiv · 1503.06714

Sampled-Data Consensus over Random Networks

Junfeng Wu·Ziyang Meng·Tao Yang·Guodong Shi·Karl H. Johansson
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Abstract

This paper considers the consensus problem for a network of nodes with random interactions and sampled-data control actions. We first show that consensus in expectation, in mean square, and almost surely are equivalent for a general random network model when the inter-sampling interval and network size satisfy a simple relation. The three types of consensus are shown to be simultaneously achieved over an independent or a Markovian random network defined on an underlying graph with a directed spanning tree. For both independent and Markovian random network models, necessary and sufficient conditions for mean-square consensus are derived in terms of the spectral radius of the corresponding state transition matrix. These conditions are then interpreted as the existence of critical value on the inter-sampling interval, below which global mean-square consensus is achieved and above which the system diverges in mean-square sense for some initial states. Finally, we establish an upper bound on the inter-sampling interval below which almost sure consensus is reached, and a lower bound on the inter-sampling interval above which almost sure divergence is reached. Some numerical simulations are given to validate the theoretical results and some discussions on the critical value of the inter-sampling intervals for the mean-square consensus are provided.

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BibTeXRIS

Junfeng Wu, Ziyang Meng, Tao Yang, Guodong Shi, Karl H. Johansson. 2015-03-23. Sampled-Data Consensus over Random Networks. https://doi.org/10.1109/tsp.2016.2568168

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