45 citations · 147 across the 71 of their papers we have counts for
17 papers · 1 filter
Kernel-based Koopman approximants for control: Flexible sampling, error analysis, and stability
Lea Bold, Friedrich M. Philipp, Manuel Schaller +1
Data-driven techniques for analysis, modeling, and control of complex dynamical systems are on the uptake. Koopman theory provides the theoretical foundation for the popular kernel…
Data-Driven Predictive Control of Nonholonomic Robots Based on a Bilinear Koopman Realization: Data Does Not Replace Geometry
Mario Rosenfelder, Lea Bold, Hannes Eschmann +3
Advances in machine learning and the growing trend towards effortless data generation in real-world systems has led to an increasing interest for data-inferred models and data-base…
Koopman-based control of nonlinear systems with closed-loop guarantees
Robin Strässer, Julian Berberich, Manuel Schaller +2
In this paper, we provide a tutorial overview and an extension of a recently developed framework for data-driven control of unknown nonlinear systems with rigorous closed-loop guar…
Group-Convolutional Extended Dynamic Mode Decomposition
Hans Harder, Feliks Nüske, Friedrich M. Philipp +3
This paper explores the integration of symmetries into the Koopman-operator framework for the analysis and efficient learning of equivariant dynamical systems using a group-convolu…
Nonlinear controlled port-Hamiltonian systems: Existence of (optimal) solutions
Willem Esterhuizen, Bernhard Maschke, Till Preuster +2
We investigate the existence of solutions of reversible and irreversible port-Hamilto\-nian systems. To this end, we utilize the associated exergy, a function that is composed of t…
Data-driven MPC with terminal conditions in the Koopman framework
Karl Worthmann, Robin Strässer, Manuel Schaller +2
We investigate nonlinear model predictive control (MPC) with terminal conditions in the Koopman framework using extended dynamic mode decomposition (EDMD) to generate a data-based…