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System Level Synthesis

Anderson, James and Doyle, John C. and Low, Steven and Matni, Nikolai (2019) System Level Synthesis. . (Unpublished)

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This article surveys the System Level Synthesis framework, which presents a novel perspective on constrained robust and optimal controller synthesis for linear systems. We show how SLS shifts the controller synthesis task from the design of a controller to the design of the entire closed loop system, and highlight the benefits of this approach in terms of scalability and transparency. We emphasize two particular applications of SLS, namely large-scale distributed optimal control and robust control. In the case of distributed control, we show how SLS allows for localized controllers to be computed, extending robust and optimal control methods to large-scale systems under practical and realistic assumptions. In the case of robust control, we show how SLS allows for novel design methodologies that, for the first time, quantify the degradation in performance of a robust controller due to model uncertainty -- such transparency is key in allowing robust control methods to interact, in a principled way, with modern techniques from machine learning and statistical inference. Throughout, we emphasize practical and efficient computational solutions, and demonstrate our methods on easy to understand case studies.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Anderson, James0000-0002-2832-8396
Doyle, John C.0000-0002-1828-2486
Low, Steven0000-0001-6476-3048
Matni, Nikolai0000-0003-4936-3921
Record Number:CaltechAUTHORS:20190619-111522918
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96562
Deposited By: Tony Diaz
Deposited On:19 Jun 2019 18:30
Last Modified:19 Jun 2019 18:30

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