Design of sparse relative sensing networks

Simone Schuler; Daniel Zelazo; Frank Allgower

doi:10.1109/CDC.2012.6426358

Design of sparse relative sensing networks

Simone Schuler
, Daniel Zelazo
, Frank Allgower

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper considers the problem of designing sparse relative sensing networks (RSN) subject to a given H_∞-performance constraint. The topology design considers homogeneous and heterogeneous agents over weighted graphs. We develop a computationally efficient formulation of the sparse topology design via a convex ℓ₁-relaxation. This makes the proposed algorithm attractive for practical applications. We also demonstrate how this relaxation can be used to embed additional performance criteria, such as maximization of the algebraic connectivity of the RSN.

Original language	English
Article number	6426358
Pages (from-to)	2749-2754
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/CDC.2012.6426358
State	Published - 2012
Externally published	Yes
Event	51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States Duration: 10 Dec 2012 → 13 Dec 2012

Keywords

H-performance
relative sensing networks
topology design
ℓ-minimization

ASJC Scopus subject areas

Control and Systems Engineering
Modelling and Simulation
Control and Optimization

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10.1109/CDC.2012.6426358

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abstract = "This paper considers the problem of designing sparse relative sensing networks (RSN) subject to a given H∞-performance constraint. The topology design considers homogeneous and heterogeneous agents over weighted graphs. We develop a computationally efficient formulation of the sparse topology design via a convex ℓ1-relaxation. This makes the proposed algorithm attractive for practical applications. We also demonstrate how this relaxation can be used to embed additional performance criteria, such as maximization of the algebraic connectivity of the RSN.",

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AB - This paper considers the problem of designing sparse relative sensing networks (RSN) subject to a given H∞-performance constraint. The topology design considers homogeneous and heterogeneous agents over weighted graphs. We develop a computationally efficient formulation of the sparse topology design via a convex ℓ1-relaxation. This makes the proposed algorithm attractive for practical applications. We also demonstrate how this relaxation can be used to embed additional performance criteria, such as maximization of the algebraic connectivity of the RSN.

KW - H-performance

KW - relative sensing networks

KW - topology design

KW - ℓ-minimization

UR - https://www.scopus.com/pages/publications/84874282960

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DO - 10.1109/CDC.2012.6426358

M3 - Conference article

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JO - Proceedings of the IEEE Conference on Decision and Control

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M1 - 6426358

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Y2 - 10 December 2012 through 13 December 2012

ER -

Design of sparse relative sensing networks

Abstract

Keywords

ASJC Scopus subject areas

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