45 citations · 144 across the 50 of their papers we have counts for
14 papers · 1 filter
Error analysis of kernel EDMD for prediction and control in the Koopman framework
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is…
Koopman-based feedback design with stability guarantees
Robin Strässer, Manuel Schaller, Karl Worthmann +2
We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data. Our approach relies on Koopman-operator…
Data-driven MPC with stability guarantees using extended dynamic mode decomposition
Lea Bold, Lars Grüne, Manuel Schaller +1
For nonlinear (control) systems, extended dynamic mode decomposition (EDMD) is a popular method to obtain data-driven surrogate models. Its theoretical foundation is the Koopman fr…
Reprojection methods for Koopman-based modelling and prediction
Pieter van Goor, Robert Mahony, Manuel Schaller +1
Extended Dynamic Mode Decomposition (eDMD) is a powerful tool to generate data-driven surrogate models for the prediction and control of nonlinear dynamical systems in the Koopman…
Equivariance and partial observations in Koopman operator theory for partial differential equations
Sebastian Peitz, Hans Harder, Feliks Nüske +3
The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems. The main reason is the enormous potential of identifying line…
Energy-optimal control of adaptive structures
Manuel Schaller, Amelie Zeller, Michael Böhm +3
Adaptive structures are equipped with sensors and actuators to actively counteract external loads such as wind. This can significantly reduce resource consumption and emissions dur…
Optimal control of port-Hamiltonian systems: energy, entropy, and exergy
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
We consider irreversible and coupled reversible-irreversible nonlinear port-Hamiltonian systems and the respective sets of thermodynamic equilibria. In particular, we are concerned…
On the equivalence of geometric and descriptor representations of linear port-Hamiltonian systems
Hannes Gernandt, Friedrich Philipp, Till Preuster +1
We prove a one-to-one correspondence between the geometric formulation of port-Hamiltonian (pH) systems defined by Dirac structures, Lagrange structures, maximal resistive structur…
Port-Hamiltonian formulation of Oseen flows
Timo Reis, Manuel Schaller
We present Oseen equations on Lipschitz domains in a port-Hamiltonian context. Such equations arise, for instance, by linearization of the Navier-Stokes equations. In our setup, th…
Hidden regularity in singular optimal control of port-Hamiltonian systems
Timm Faulwasser, Jonas Kirchhoff, Volker Mehrmann +3
We study the problem of state transition on a finite time interval with minimal energy supply for linear port-Hamiltonian systems. While the cost functional of minimal energy suppl…
Port-Hamiltonian descriptor systems are relative generically controllable and stabilizable
Achim Ilchmann, Jonas Kirchhoff, Manuel Schaller
The present work is a successor of [Ilchmann, Kirchhoff 2022] on generic controllability and of [Ilchmann, Kirchhoff 2023] on relative generic controllability of linear differentia…
Sampled-data funnel control and its use for safe continual learning
Lukas Lanza, Dario Dennstädt, Karl Worthmann +4
We propose a novel sampled-data output-feedback controller for nonlinear systems of arbitrary relative degree that ensures reference tracking within prescribed error bounds. We pro…
Infinite-dimensional port-Hamiltonian systems -- a system node approach
Friedrich Philipp, Timo Reis, Manuel Schaller
We consider an operator-theoretic approach to linear infinite-dimensional port-Hamiltonian systems. In particular, we use the theory of system nodes by Staffans to formulate a~suit…
Error bounds for kernel-based approximations of the Koopman operator
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation…