Convex relaxations and Gramian rank constraints for sensor and actuator selection in networks

Tyler Summers*, Iman Shames

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

We propose a convex relaxation heuristic for sensor and actuator selection problems in dynamical networks using Gramian metrics. We also propose heuristic algorithms to enforce a rank constraint on the Gramian that can be used in conjunction with combinatorial greedy algorithms and the convex relaxation. This allows selection of sensor or actuator sets that optimize an objective function while preserving a certain amount of observability or controllability throughout the state space, combining previous methods that focus exclusively on either rank or Gramian metrics. We illustrate and compare the greedy and convex relaxation heuristics in several numerical examples involving random and regular networks.

Original languageEnglish
Title of host publication2016 IEEE International Symposium on Intelligent Control, ISIC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509020515
DOIs
Publication statusPublished - 29 Sept 2016
Externally publishedYes
Event2016 IEEE International Symposium on Intelligent Control, ISIC 2016 - Buenos Aires, Argentina
Duration: 19 Sept 201622 Sept 2016

Publication series

NameIEEE International Symposium on Intelligent Control - Proceedings
Volume2016-September

Conference

Conference2016 IEEE International Symposium on Intelligent Control, ISIC 2016
Country/TerritoryArgentina
CityBuenos Aires
Period19/09/1622/09/16

Fingerprint

Dive into the research topics of 'Convex relaxations and Gramian rank constraints for sensor and actuator selection in networks'. Together they form a unique fingerprint.

Cite this