Automating control system design: Using language models for expert knowledge in decentralized controller auto-tuning

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dc.contributor.authorAres-Milian, Marlon J.
dc.contributor.authorProvan, Gregory
dc.contributor.authorQuinones-Grueiro, Marcos
dc.contributor.funderScience Foundation Ireland(SFI)
dc.date.accessioned2026-05-22T08:50:02Z
dc.date.available2026-05-22T08:50:02Z
dc.date.issued2025-11-10
dc.description.abstractFully-automated optimal controller design for engineering systems is a challenging task. While, optimization-based, automated control parameter tuning techniques have been widely discussed in the literature, most works do not discuss expert knowledge requirements for system design, which result in significant human intervention. In this work, we discuss a multistage controller tuning framework for decentralized control that highlights expert knowledge requirements in automated controller design. We propose a methodology to automate the input-output pairing and stage definition steps in the framework using Large Language Models (LLMs) for a family of multi-tank benchmarks. We achieve this by proposing a mathematical language to describe the system and design an algorithm to bind this mathematical representation to the input prompt space of an LLM. We demonstrate that our methodology can produce consistent expert knowledge outputs from the LLM with over 97% accuracy for the multi-tank benchmarks. We also empirically show that, correct stage definition by the LLM can improve tuned controller performance by up to 52%.en
dc.description.sponsorshipThis work was supported by Science Foundation Ireland under Grant 13/RC/2094
dc.description.versionPublished Version
dc.format.extent20
dc.format.mimetypeapplication/pdfen
dc.identifier.authororcidAres-Milian, Marlon J.
dc.identifier.authororcidProvan, Gregory§0009-0000-6794-7619
dc.identifier.authororcidQuinones-Grueiro, Marcos
dc.identifier.citationAres-Milian, M J, Provan, G & Quinones-Grueiro, M 2025, 'Automating control system design: Using language models for expert knowledge in decentralized controller auto-tuning', Paper presented at 36th International Conference on Principles of Diagnosis and Resilient Systems, DX 2025, Nashville, United States, 22/09/25 - 24/09/25 pp. 1-20. https://doi.org/10.4230/OASIcs.DX.2025.10
dc.identifier.doi10.4230/OASIcs.DX.2025.10
dc.identifier.endpage20
dc.identifier.otherORCID: /0009-0000-6794-7619/work/215517478
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18815
dc.language.isoen
dc.relation.urihttps://www.scopus.com/pages/publications/105038002290
dc.rights© 2025, Marlon J. Ares-Milian, Gregory Provan, and Marcos Quinones-Grueiro; licensed under Creative Commons License CC-BY 4.0.
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectAutomated system design
dc.subjectController auto-tuning
dc.subjectLarge language models
dc.subject[FoodNutritionalSciences]
dc.titleAutomating control system design: Using language models for expert knowledge in decentralized controller auto-tuningen
dc.typeConference item
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