Institut für Regelungstechnik

Systems & Control Seminar

Am Institut für Regelungstechnik finden im Rahmen des Systems & Control Seminars regelmäßig Vorträge von international renommierten Forscherinnen und Forschern zu Themen statt, die in direktem Zusammenhang zu den Forschungsschwerpunkten des Instituts stehen. Die folgende Liste enthält die vergangenen und die aktuell geplanten Vorträge des Seminars.

Data-driven predictive control with guarantees exemplified in the Koopman framework (Prof. Worthmann, Technische Universität Ilmenau, Germany)

Single timescale actor critic: a small-gain analysis (Prof. Bahman Gharesifard, Department of Mathematics and Statistics, Queen`s University

To Trigger or Not to Trigger: Exploring the Theory of Event-Based Control (Prof. Frank Allgöwer, University of Stuttgart)

Modeling, Simulation and Automatic Control of Thyroid Autoimmune Diseases (Prof. Markus Reichhartinger, Institute for Automation and Control, Graz University of Technology)

Learning controllers for machines: Paradigms and recent results (Prof. Sebastian Trimpe, Institut für Data Science in Maschinenbau, RWTH Aachen University)

Can neural networks solve high dimensional optimal feedback control problems? (Prof. Lars Grüne, Applied Mathematics, University of Bayreuth)

A hybrid systems framework for data-based adaptive control of linear time-varying systems (Prof. Andrea Ianelli, Institute for Systems Theory and Automatic Control, University of Stuttgart)

Derivative-free Learning of Interpretable Control Policies (Prof. Ali Mesbah, Department of Chemical and Biomolecular Engineering, University of California Berkeley)

Polynomial orthogonal bases for data-driven analysis and control of continuous-time systems (Prof. Paolo Rapisarda, University of Southampton)

Feedback Optimization for Socio-Physical Networks (Prof. Gianluca Bianchin, ICTEAM & Department Mathematical Engineering University of Louvain)

Data-driven prediction and control with stochastic data: a system identification perspective (Yin Mingzhou, Automatic Control Laboratory, ETH Zürich)

Safe, Stable and Explainable Reinforcement Learning (Prof. Mario Zanon, IMT School for Advanced Studies Lucca)

Data-Driven Approaches to Stochastic Linear Systems? Willems' Fundamental Lemma Through the Eyes of Wiener (Prof. Timm Faulwasser, Institute for Energy Systems, TU Dortmund)

Hybrid multi-observer for improving estimation performance (Elena Petri, Université de Lorraine)

Learning to control: history and challenges of direct data-driven design (Prof. Simone Formentin, Politecnico di Milano)

A formula for data-driven control of nonlinear systems (Prof. Claudio De Persis, University of Groningen)

Data-driven dynamic interpolation and approximation: System theory without transfer function and state space representations (Prof. Ivan Markovsky, Vrije Universiteit Brussel)

Safe Learning-based Control of Mechanical Systems (Dr.-Ing. Thomas Beckers, University of Pennsylvania)

How to Address Uncertainty in Control Systems: Transferring Theory to Application in Robotics (Prof. Erdal Kayacan, Aarhus University)

A scale-free small-gain theorem for hyper-connected networks (Dr. Navid Noroozi, Otto von Guericke University Magdeburg)

Communication Reduction in Distributed Systems (Tobias Sprodowski, University of Bremen)

Minimax strategy in approximate model predictive control (Andrei Pavlov, University of Melbourne)

Model Predictive Control Paradigms for Industrial Applications (Dr. Fahad Albalawi, King Abdullah University of Science and Technology)

Letzte Änderung: 15.02.26 Druckversion