Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient

[2403.05367] Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient 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:2403.05367 (eess) [Submitted on 8 Mar 2024 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient Authors: Lorenzo Sforni , Guido Carnevale , Ivano Notarnicola , Giuseppe Notarstefano View a PDF of the paper titled Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient, by Lorenzo Sforni and 3 other authors View PDF HTML (experimental) Abstract: In this paper, we investigate a data-driven framework to solve Linear Quadratic Regulator (LQR) problems when the dynamics is unknown, with the additional challenge of providing stability certificates for the overall learning and control scheme. Specifically, in the proposed on-policy learning framework, the control input is applied to the actual (unknown) linear system while iteratively optimized. We propose a learning and control procedure, termed Relearn LQR, that combines a recursive least squares method with a direct policy search based on the gradient method. The resulting scheme is analyzed by modeling it as a feedback-interconnected nonlinear dynamical system. A Lyapunov-based approach, exploiting averaging and timescale separation theories for nonlinear systems, allows us to provide formal stability guarantees for the whole interconnected scheme. The effectiveness of the proposed strategy is corroborated by numerical simulations, where Relearn LQR is deployed on an aircraft control problem, with both static and drifting parameters. Subjects: Systems and Control (eess.SY) Cite as: arXiv:2403.05367 [eess.SY] (or arXiv:2403.05367v3 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2403.05367 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Guido Carnevale [ view email ] [v1] Fri, 8 Mar 2024 14:58:34 UTC (584 KB) [v2] Tue, 18 Feb 2025 13:29:41 UTC (1,330 KB) [v3] Fri, 10 Apr 2026 10:44:15 UTC (775 KB) Full-text links: Access Paper: View a PDF of the paper titled Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient, by Lorenzo Sforni and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2024-03 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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