2 citations · 4 across the 9 of their papers we have counts for
9 papers
A Continuous-Time Generalization of the LPV Fundamental Lemma
Philipp Schmitz, Chris Verhoek
Controller design for physical systems directly from data is of tremendous interest in both the industry and academia. However, the research on direct data-driven control has thus…
Data-driven feedback rectification of switched linear systems
Philipp Schmitz, Hannes Gernandt, Maria C. Honecker +1
In this paper, a data-driven method for the computation of stabilizing state-feedbacks is proposed that leads to a rectified eigenstructure of switched linear systems. This means t…
A data-based image representation for continuous-time LTI systems
Amine Othmane, Philipp Schmitz, Karl Worthmann +1
We derive a numerically stable method to compute an image representation of an unknown linear system only from data, leveraging a continuous-time version of Willems et al.'s fundam…
Excitation of control-affine systems and Koopman error bounds
Philipp Schmitz, Lea Bold, Friedrich M. Philipp +4
The Koopman operator and extended dynamic mode decomposition (EDMD) as a data-driven technique for its approximation have attracted considerable attention as a key tool for modelin…
Towards Data-Driven Multi-Stage OPF
Oleksii Molodchyk, Philipp Schmitz, Alexander Engelmann +2
The operation of large-scale power systems is usually scheduled ahead via numerical optimization. However, this requires models of grid topology, line parameters, and bus specifica…
A continuous-time fundamental lemma and its application in data-driven optimal control
Philipp Schmitz, Timm Faulwasser, Paolo Rapisarda +1
Data-driven control of discrete-time and continuous-time systems is of tremendous research interest. In this paper, we explore data-driven optimal control of continuous-time linear…
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…
Safe data-driven reference tracking with prescribed performance
Philipp Schmitz, Lukas Lanza, Karl Worthmann
We study output reference tracking for unknown continuous-time systems with arbitrary relative degree. The control objective is to keep the tracking error within predefined time-va…
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…