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Robust STL Control Synthesis under Maximal Disturbance Sets
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-3627-7103
University of Southern California, Thomas Lord Department of Computer Science, Los Angeles, CA, USA.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-4173-2593
2024 (English)In: 2024 IEEE 63rd Conference on Decision and Control, CDC 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 315-321Conference paper, Published paper (Refereed)
Abstract [en]

This work addresses maximally robust control synthesis under unknown disturbances. We consider a nonlinear system, subject to a Signal Temporal Logic (STL) specification and jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances yet, to the authors' best knowledge, this is the first work that aims to maximize the permissible disturbance set and find corresponding maximally robust controllers. We, therefore, introduce disturbance robustness as a model-based robustness metric for STL planning and control synthesis. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm for maximally robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. p. 315-321
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-361740DOI: 10.1109/CDC56724.2024.10886238Scopus ID: 2-s2.0-86000657163OAI: oai:DiVA.org:kth-361740DiVA, id: diva2:1948007
Conference
63rd IEEE Conference on Decision and Control, CDC 2024, Milan, Italy, Dec 16 2024 - Dec 19 2024
Note

 Part of ISBN 9798350316339

QC 20250401

Available from: 2025-03-27 Created: 2025-03-27 Last updated: 2025-04-01Bibliographically approved

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Verhagen, JorisTumova, Jana

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