Koopman-based controller design for nonlinear systems with closed-loop guarantees

Erscheinungsjahr 2026
ISBN: 978-3-8325-6088-1
Verlag: Logos

Buch, Englisch, 187 Seiten, PB, Format (B × H): 145 mm x 210 mm

Koopman operator theory has emerged as a powerful tool for data-driven analysis and control of nonlinear dynamical systems, enabling (bi)linear representations of complex dynamics. Its practical application requires finite-dimensional approximations, which inevitably introduce model errors. Existing approaches often neglect these errors, limiting the reliability of Koopman-based control.

This thesis closes this gap by presenting a systematic control framework for nonlinear systems that connects Koopman theory and data-driven modeling to robust control design with rigorous closed-loop guarantees. The framework consists of two main components. First, bilinear surrogate models are derived from data using Koopman operator theory, providing explicit residual error bounds that account for finite-data and approximation effects. Second, these surrogate models form the basis for designing robust state-feedback controllers that explicitly incorporate the model uncertainties, ensuring desired closed-loop properties of the underlying nonlinear system.

The framework in this thesis provides a rigorous and systematic foundation for Koopman-based control, establishing both numerical effectiveness and theoretical closed-loop guarantees, such as exponential stability and quadratic performance, via linear matrix inequalities and sum-of-squares relaxations.

Autoren/Hrsg.

Strässer, Robin

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung

Über Autor(innen)

Robin Strässer received his Bachelor's and Master's degrees in Simulation Technology from the University of Stuttgart in 2018 and 2020, respectively. From 2020 to 2026, he was a research assistant at the Institute for Systems Theory and Automatic Control at the University of Stuttgart under the supervision of Prof. Frank Allgöwer. During this time, he completed his PhD with distinction. His research interests lie in data-driven system analysis and control for nonlinear systems, with a particular focus on merging Koopman operator theory and robust control.