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20232026
most citedFast and memory-efficient optimization for large-scale data-driven predictive control

1 citations · 1 across the 7 of their papers we have counts for

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math.OC2026

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…

math.OC2026

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…

math.OC2024

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…

math.OC20241 cited

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…

math.OC2023

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…