Distributed differentiation with noisy measurements for exact dynamic consensus

被引：0

作者：

Aldana-Lopez, Rodrigo ^{[1
,2
]}

Aragues, Rosario ^{[1
,2
]}

Sagues, Carlos ^{[1
,2
]}

机构：

[1] Univ Zaragoza, DIIS, Zaragoza 50018, Spain

[2] Univ Zaragoza, I3A, Zaragoza 50018, Spain

来源：

IFAC PAPERSONLINE | 2023年 / 56卷 / 02期

关键词：

Consensus; Multi- agent systems; Sensor networks; AVERAGE; SIGNALS;

D O I：

10.1016/j.ifacol.2023.10.1101

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work is devoted to a dynamic consensus problem referred here as distributed differentiation, which consists on estimating high order derivatives of the average of a group of signals in a decentralized fashion. To do so, a distributed differentiation protocol is proposed which solves the problem with exact convergence in the noiseless case. Moreover, the protocol allows to compute weighted averages according to quality or noise levels of individual signals. Different from previous approaches, our proposal no longer requires additional local differentiators. In addition, we provide a detailed formal analysis of the performance of the protocol under noisy measurements. Simulation examples are provided to show the effectiveness of our proposal. Copyright (c) 2023 The Authors.

引用

页码：2038 / +

页数：7

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