45 citations · 144 across the 50 of their papers we have counts for
59 papers
Optimal energy conversion -- A port-Hamiltonian approach
Guanru Pan, Karl Worthmann, Manuel Schaller +1
This paper studies optimal energy conversion in port-Hamiltonian systems. We analyze state- and control-constrained finite-horizon optimal control problems that maximize energy con…
OmniPoser: Flexible Human Motion Recovery in the Wild with Masked Flow Matching
Minghao Liu, Tutian Tang
Recovering 3D human motion from sparse wearable sensors is challenging due to the inherent noise, occlusion, and instability of real-world tracking signals. Existing approaches are…
Energy-optimal predictive control of discrete-time port-Hamiltonian systems: Closed-loop practical stability
Vaibhav Kumar Singh, Manuel Schaller, Timm Faulwasser +1
We study a dissipativity-based model predictive control (MPC) scheme for energy-optimal constrained output stabilization of a discrete-time nonlinear SISO port-Hamiltonian system d…
Extended dynamic mode decomposition with Fourier dictionaries: Error bounds and fast implementation
Felix Bartel, Sandra Ritter, Manuel Schaller +1
The Koopman operator has gained considerable attention in dynamical systems due to its capability to provide a linear viewpoint for nonlinear systems using data-driven methods such…
A constraint dissolving inexact penalty method for optimization problems with geometric constraints
Xiaoxi Jia, Leander Lerch, Stefan Streif +1
Optimization problems with geometric constraints have a broad range of applications, including machine learning, finance, and control. A powerful algorithmic tool to resolve these…
Verifiable Regularity Criterion for Conditional Expectation Operators and Conditional Mean Embeddings with Applications to Nonparametric Regression, Bayesian Inverse Problems, and Koopman Operators
Maximiliano Hertel, Ilja Klebanov, Manuel Schaller +1
Conditional expectation operators (CEOs) and their associated conditional mean embeddings (CMEs) play a central role across applied mathematics and machine learning, appearing in n…
Koopman meets input-output data: Data-driven output-feedback control of nonlinear systems with closed-loop guarantees
Robin Strässer, Julian Berberich, Manuel Schaller +2
Data-driven control of nonlinear systems from input-output measurements remains a fundamental challenge, as existing approaches with rigorous closed-loop guarantees predominantly r…
Koopman for stochastic dynamics: error bounds for kernel extended dynamic mode decomposition
Maximiliano Hertel, Friedrich M. Philipp, Manuel Schaller +1
We prove -error bounds for kernel extended dynamic mode decomposition (kEDMD) approximants of the Koopman operator for stochastic dynamical systems. To this end, we estab…
An overview of Koopman-based control: From error bounds to closed-loop guarantees
Robin Strässer, Karl Worthmann, Igor Mezić +3
Controlling nonlinear dynamical systems remains a central challenge in a wide range of applications, particularly when accurate first-principle models are unavailable. Data-driven…
Energy-optimal control of discrete-time port-Hamiltonian systems
Arijit Sarkar, Vaibhav Kumar Singh, Manuel Schaller +1
In this letter, we study the energy-optimal control of nonlinear port-Hamiltonian (pH) systems in discrete time. For continuous-time pH systems, energy-optimal control problems are…
Maximum-likelihood reprojections for reliable Koopman predictors of parametric nonlinear dynamical systems
Pieter van Goor, Robert Mahony, Manuel Schaller +1
Koopman-based methods leverage a nonlinear lifting to enable linear regression techniques. Consequently, system analysis, learning, and prediction are considered through the lens o…
Data-driven Model Predictive Control: Asymptotic Stability despite Approximation Errors exemplified in the Koopman framework
Irene Schimperna, Karl Worthmann, Manuel Schaller +2
In this paper, we analyze stability of nonlinear model predictive control (MPC) using data-driven surrogate models in the optimization step. First, we establish asymptotic stabilit…
Kernel-based error bounds of bilinear Koopman surrogate models for nonlinear data-driven control
Robin Strässer, Manuel Schaller, Julian Berberich +2
We derive novel deterministic bounds on the approximation error of data-based bilinear surrogate models for unknown nonlinear systems. The surrogate models are constructed using ke…
Spatial decay of perturbations in hyperbolic equations with optimal boundary control
Benedikt Oppeneiger, Manuel Schaller, Karl Worthmann
Recently, domain-uniform stabilizability and detectability has been the central assumption %in order robustness results on the to ensure robustness in the sense of exponential deca…
Spatial exponential decay of perturbations in optimal control of general evolution equations
Simone Göttlich, Benedikt Oppeneiger, Manuel Schaller +1
We analyze the robustness of optimally controlled evolution equations with respect to spatially localized perturbations. We prove that if the involved operators are domain-uniforml…
Kernel EDMD for data-driven nonlinear Koopman MPC with stability guarantees
Lea Bold, Manuel Schaller, Irene Schimperna +1
Extended dynamic mode decomposition (EDMD) is a popular data-driven method to predict the action of the Koopman operator, i.e., the evolution of an observable function along the fl…
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…