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SDP-based joint sensor and controller design for information-regularized optimal LQG control
KTH, School of Electrical Engineering (EES), Automatic Control.
2016 (English)In: Proceedings of the IEEE Conference on Decision and Control, IEEE conference proceedings, 2016, 4486-4491 p.Conference paper (Refereed)Text
Abstract [en]

We consider a joint sensor and controller design problem for linear Gaussian stochastic systems in which a weighted sum of quadratic control cost and the amount of information acquired by the sensor is minimized. This problem formulation is motivated by situations where a control law must be designed in the presence of sensing, communication, and privacy constraints. We show that an optimal linear joint sensor-controller policy is comprised of a linear sensor, Kalman filter, and a certainty equivalence controller, and can be synthesized by a numerically efficient algorithm based on semidefinite programming (SDP).

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 4486-4491 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-188287DOI: 10.1109/CDC.2015.7402920ScopusID: 2-s2.0-84962020013ISBN: 9781479978861OAI: oai:DiVA.org:kth-188287DiVA: diva2:936290
Conference
54th IEEE Conference on Decision and Control, CDC 2015, 15 December 2015 through 18 December 2015
Note

QC 20160613

Available from: 2016-06-13 Created: 2016-06-09 Last updated: 2016-06-13Bibliographically approved

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Publisher's full textScopushttp://cdc2015.ieeecss.org/

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Sandberg, Henrik
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ReferencesLink to record
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