45 citations · 144 across the 50 of their papers we have counts for
7 papers · 1 filter
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…