Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition

[2502.13042] Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Systems and Control arXiv:2502.13042 (eess) [Submitted on 18 Feb 2025 ( v1 ), last revised 10 Apr 2026 (this version, v4)] Title: Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition Authors: Andrei Sperilă , Alessio Iovine , Sorin Olaru , Patrick Panciatici View a PDF of the paper titled Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition, by Andrei Speril\u{a} and 3 other authors View PDF HTML (experimental) Abstract: A two-layer control architecture is proposed to enable scalable implementations for constraint-based decision strategies, such as model predictive controllers. The bottom layer is based upon a distributed feedback-feedforward scheme that directs the controlled network's information flow according to a pre-specified communication infrastructure. Explicit expressions for the resulting closed-loop maps are obtained, and an offline model-matching procedure is proposed for designing the first layer. The obtained control laws are deployed via distributed state-space-based implementations, and the resulting closed-loop models enable predictive control design for the constraint management procedure described in our companion paper. Comments: 20 pages, 5 figures Subjects: Systems and Control (eess.SY) Cite as: arXiv:2502.13042 [eess.SY] (or arXiv:2502.13042v4 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2502.13042 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Andrei Sperilă [ view email ] [v1] Tue, 18 Feb 2025 16:55:03 UTC (61 KB) [v2] Sun, 9 Nov 2025 13:20:02 UTC (67 KB) [v3] Fri, 13 Feb 2026 16:11:05 UTC (67 KB) [v4] Fri, 10 Apr 2026 07:40:08 UTC (68 KB) Full-text links: Access Paper: View a PDF of the paper titled Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition, by Andrei Speril\u{a} and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2025-02 Change to browse by: cs cs.SY eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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