@inproceedings{bd76290df8f842b1969e7f10c041c0bd,
title = "Almost sure averagingwith relative-state-dependent measurement noises and linear noise intensity functions",
abstract = "In this paper, we consider the distributed averaging of high-dimensional first-order agents with relative-state-dependent measurement noises. Each agent can measure or receive its neighbors' state information with random noises, whose intensity is a linear vector-valued function of agents' relative states. Differently from the case with non-state-dependent measurement noises, we show that a negative control gain, though can not ensure mean square consensus, may ensure almost sure consensus. This tells us that the relative-state-dependent measurement noises will sometimes be helpful for the almost sure consensus of the network. For symmetric measurement models, the almost sure convergence rate is estimated by the Iterated Logarithm Law of Brownian motions.",
keywords = "Consensus, Distributed Averaging, Measurement Noise, Multi-Agent System",
author = "Tao Li and Fuke Wu",
note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; Proceedings of the 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",
year = "2014",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2014.6896806",
language = "英语",
series = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
publisher = "IEEE Computer Society",
pages = "1242--1246",
editor = "Shengyuan Xu and Qianchuan Zhao",
booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
address = "美国",
}