Imperial College London
Portrait Picture

Prof David Angeli

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Nonlinear Network Dynamics
 
 
 
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Contact

 

+44 (0)20 7594 6283d.angeli Website

 
 
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Location

 

1107CElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Manfredi:2020:10.1109/TCNS.2019.2906865,
author = {Manfredi, S and Angeli, D},
doi = {10.1109/TCNS.2019.2906865},
journal = {IEEE Transactions on Control of Network Systems},
pages = {372--383},
title = {Robust distributed estimation of the maximum of a field},
url = {http://dx.doi.org/10.1109/TCNS.2019.2906865},
volume = {7},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper deals with the problem of robust distributed sampling of a field in the presence of unreliable sensors/agents. An algorithm is devised to estimate the maximum of the field over the domain spanned by the agents where some of the sensors can sample wrong measurements over a finite time, higher than the maximum field value. Necessary and sufficient conditions are given to guarantee convergence to the maximum field value and a robust and redundant algorithm design is presented by combining an exhaustive ergodic search with multiagent consensus protocols. In this original setup, the presence of unilateral interactions and exogenous signals is considered, the latter representing the measures sampled by the agents. Representative examples are presented to illustrate the effectiveness of the proposed framework and conditions.
AU  - Manfredi,S
AU  - Angeli,D
DO  - 10.1109/TCNS.2019.2906865
EP  - 383
PY  - 2020///
SN  - 2325-5870
SP  - 372
TI  - Robust distributed estimation of the maximum of a field
T2  - IEEE Transactions on Control of Network Systems
UR  - http://dx.doi.org/10.1109/TCNS.2019.2906865
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000521969300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8675441
UR  - http://hdl.handle.net/10044/1/83197
VL  - 7
ER  -
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