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Localized LQR Control with Actuator Regularization

Wang, Yuh-Shyang and Matni, Nikolai and Doyle, John C. (2016) Localized LQR Control with Actuator Regularization. In: 2016 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 5205-5212. ISBN 978-1-4673-8680-7 . https://resolver.caltech.edu/CaltechAUTHORS:20160802-095819362

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Abstract

In previous work, we posed and solved the localized linear quadratic regulator (LLQR) problem - a LLQR controller is one that limits the propagation of dynamics to user-specified subsets of the global system. The advantages of taking this approach are tangible, as we show that this allows the controller to be synthesized and implemented in a scalable local manner. Implicit in this previous work was the existence of a feasible spatio-temporal constraint on the controller and closed loop response of the system that enforced these locality properties. This paper proposes and analyzes a procedure for designing such a spatio-temporal constraint, which can be interpreted as a measure of the implementation complexity of a controller, and a sparse actuation architecture that ensures that it is feasible. We show that the computational tasks involved can be suitably decomposed and solved using the alternating direction method of multipliers (ADMM), thus providing a scalable approach to designing a LLQR controller with a sparse actuation architecture.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/ACC.2016.7526485DOIArticle
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7526485&isnumber=7524873PublisherArticle
ORCID:
AuthorORCID
Wang, Yuh-Shyang0000-0001-7357-7247
Matni, Nikolai0000-0003-4936-3921
Doyle, John C.0000-0002-1828-2486
Additional Information:© 2016 AACC. This research was in part supported by NSF NetSE, AFOSR, the Institute for Collaborative Biotechnologies through grant W911NF-09-0001 from the U.S. Army Research Office, and from MURIs “Scalable, Data-Driven, and Provably-Correct Analysis of Networks” (ONR) and “Tools for the Analysis and Design of Complex Multi-Scale Networks” (ARO). The content does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.
Funders:
Funding AgencyGrant Number
NSFUNSPECIFIED
Air Force Office of Scientific Research (AFOSR)UNSPECIFIED
Army Research Office (ARO)W911NF-09-0001
Office of Naval Research (ONR)UNSPECIFIED
Record Number:CaltechAUTHORS:20160802-095819362
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160802-095819362
Official Citation:Y. S. Wang, N. Matni and J. C. Doyle, "Localized LQR control with actuator regularization," 2016 American Control Conference (ACC), Boston, MA, USA, 2016, pp. 5205-5212. doi: 10.1109/ACC.2016.7526485
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:69382
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:02 Aug 2016 23:26
Last Modified:03 Oct 2019 10:21

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