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Data-Driven Distributed and Localized Model Predictive Control

Alonso, Carmen Amo and Yang, Fengjun and Matni, Nikolai (2022) Data-Driven Distributed and Localized Model Predictive Control. IEEE Open Journal of Control Systems, 1 . pp. 29-40. ISSN 2694-085X. doi:10.1109/OJCSYS.2022.3171787. https://resolver.caltech.edu/CaltechAUTHORS:20220628-155447293

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Abstract

Motivated by large-scale but computationally constrained settings, e.g., the Internet of Things, we present a novel data-driven distributed control algorithm that is synthesized directly from trajectory data. Our method, data-driven Distributed and Localized Model Predictive Control (D³LMPC), builds upon the data-driven System Level Synthesis (SLS) framework, which allows one to parameterize closed-loop system responses directly from collected open-loop trajectories. The resulting model-predictive controller can be implemented with distributed computation and only local information sharing. By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system. Moreover, we show that our algorithm enjoys theoretical guarantees for recursive feasibility and asymptotic stability. Finally, we also demonstrate the optimality and scalability of our algorithm in a simulation experiment.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/OJCSYS.2022.3171787DOIArticle
https://ieeexplore.ieee.org/document/9772975PublisherArticle
ORCID:
AuthorORCID
Alonso, Carmen Amo0000-0001-7593-5992
Yang, Fengjun0000-0003-4874-2225
Matni, Nikolai0000-0003-4936-3921
Additional Information:© 2022 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. Received 21 December 2021; revised 13 March 2022; accepted 8 April 2022. Date of publication 11 May 2022; date of current version 9 June 2022. The review of this paper was arranged by Associate Editor Solmaz S. Kia. Carmen Amo Alonso and Fengjun Yang contributed equally to this work. The work of Nikolai Matni was supported in part by NSF award CPS-2038873, in part by CAREER award ECCS-2045834, and in part by a Google Research Scholar award. The work of Carmen Amo Alonso was supported by a Caltech/Amazon AI4Science fellowship. The work of Fengjun Yang was supported in part by NSF CAREER award ECCS-2045834.
Funders:
Funding AgencyGrant Number
NSFCPS-2038873
NSFECCS-2045834
Google Research AwardUNSPECIFIED
Amazon AI4Science FellowshipUNSPECIFIED
Subject Keywords:Data-driven optimization, decentralized/distributed control, large-scale systems, optimal control
DOI:10.1109/OJCSYS.2022.3171787
Record Number:CaltechAUTHORS:20220628-155447293
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220628-155447293
Official Citation:C. A. Alonso, F. Yang and N. Matni, "Data-Driven Distributed and Localized Model Predictive Control," in IEEE Open Journal of Control Systems, vol. 1, pp. 29-40, 2022, doi: 10.1109/OJCSYS.2022.3171787
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115279
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Jun 2022 19:15
Last Modified:28 Jun 2022 19:15

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