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Title: Distributed resilient filtering of large-scale systems with channel scheduling (English)
Author: Xu, Lili
Author: Zhang, Sunjie
Author: Wang, Licheng
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 56
Issue: 1
Year: 2020
Pages: 170-188
Summary lang: English
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Category: math
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Summary: This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter. (English)
Keyword: distributed filtering
Keyword: large-scale systems
Keyword: energy constraints
Keyword: sensor networks
Keyword: power systems
MSC: 93A15
MSC: 93C55
idZBL: Zbl 07217216
idMR: MR4091789
DOI: 10.14736/kyb-2020-1-0170
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Date available: 2020-05-20T15:40:44Z
Last updated: 2021-03-29
Stable URL: http://hdl.handle.net/10338.dmlcz/148102
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