SDP-based joint sensor and controller design for information-regularized optimal LQG control
2016 (English)In: Proceedings of the IEEE Conference on Decision and Control, IEEE conference proceedings, 2016, 4486-4491 p.Conference paper (Refereed)Text
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.
IdentifiersURN: urn:nbn:se:kth:diva-188287DOI: 10.1109/CDC.2015.7402920ScopusID: 2-s2.0-84962020013ISBN: 9781479978861OAI: oai:DiVA.org:kth-188287DiVA: diva2:936290
54th IEEE Conference on Decision and Control, CDC 2015, 15 December 2015 through 18 December 2015
QC 201606132016-06-132016-06-092016-06-13Bibliographically approved