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Published May 11, 2022 | Published
Journal Article Open

Data-Driven Distributed and Localized Model Predictive Control

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.

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.

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Additional details

Created:
August 20, 2023
Modified:
October 24, 2023