45 citations · 144 across the 50 of their papers we have counts for
12 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…
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…
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…
Modularized data-driven approximation of the Koopman operator and generator
Yang Guo, Manuel Schaller, Karl Worthmann +1
Extended Dynamic Mode Decomposition (EDMD) is a widely-used data-driven approach to learn an approximation of the Koopman operator. Consequently, it provides a powerful tool for da…
Invariance of Gaussian RKHSs under Koopman operators of stochastic differential equations with constant matrix coefficients
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
We consider the Koopman operator semigroup associated with stochastic differential equations of the form with constant matrices and…
-error bounds for approximations of the Koopman operator by kernel extended dynamic mode decomposition
Frederik Köhne, Friedrich M. Philipp, Manuel Schaller +2
Extended dynamic mode decomposition (EDMD) is a well-established method to generate a data-driven approximation of the Koopman operator for analysis and prediction of nonlinear dyn…
Perturbations in PDE-constrained optimal control decay exponentially in space
Simone Göttlich, Manuel Schaller, Karl Worthmann
For linear-quadratic optimal control problems (OCPs) governed by elliptic and parabolic partial differential equations (PDEs), we investigate the impact of perturbations on optimal…
Fast and memory-efficient optimization for large-scale data-driven predictive control
Philipp Schmitz, Manuel Schaller, Matthias Voigt +1
Recently, data-enabled predictive control (DeePC) schemes based on Willems' fundamental lemma have attracted considerable attention. At the core are computations using Hankel-like…
SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
Robin Strässer, Manuel Schaller, Karl Worthmann +2
The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode…
Variance representations and convergence rates for data-driven approximations of Koopman operators
Friedrich M. Philipp, Manuel Schaller, Septimus Boshoff +3
We rigorously derive novel error bounds for extended dynamic mode decomposition (EDMD) to approximate the Koopman operator for discrete- and continuous time (stochastic) systems; b…
Linear-quadratic optimal control for infinite-dimensional input-state-output systems
Timo Reis, Manuel Schaller
We examine the minimization of a quadratic cost functional composed of the output and the final state of abstract infinite-dimensional evolution equations in view of existence of s…