Zobrazeno 1 - 10
of 1 531
pro vyhledávání: '"Pfefferkorn P"'
Autor:
Pfefferkorn, Maik, Findeisen, Rolf
Employing model predictive control to systems with unbounded, stochastic disturbances poses the challenge of guaranteeing safety, i.e., repeated feasibility and stability of the closed-loop system. Especially, there are no strict repeated feasibility
Externí odkaz:
http://arxiv.org/abs/2410.08186
Autor:
Hirt, Sebastian, Höhl, Andreas, Pohlodek, Johannes, Schaeffer, Joachim, Pfefferkorn, Maik, Braatz, Richard D., Findeisen, Rolf
Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitable cost functions. To address these challenges, we discuss an approach
Externí odkaz:
http://arxiv.org/abs/2410.04982
Safe learning of control policies remains challenging, both in optimal control and reinforcement learning. In this article, we consider safe learning of parametrized predictive controllers that operate with incomplete information about the underlying
Externí odkaz:
http://arxiv.org/abs/2409.10171
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering cl
Externí odkaz:
http://arxiv.org/abs/2404.12187
We analyse the conservatism and regret of distributionally robust (DR) stochastic model predictive control (SMPC) when using moment-based ambiguity sets for modeling unknown uncertainties. To quantify the conservatism, we compare the deterministic co
Externí odkaz:
http://arxiv.org/abs/2309.12190
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. A multi-
Externí odkaz:
http://arxiv.org/abs/2303.04725
Autor:
Julia Pfefferkorn
Publikováno v:
Plato, Vol 25 (2024)
Externí odkaz:
https://doaj.org/article/3a3e4a27ea7a4adaad707948ec23ea6a
Autor:
Julia Fischer, Lukas Egli, Juliane Groth, Caterina Barrasso, Steffen Ehrmann, Heiko Figgemeier, Christin Henzen, Carsten Meyer, Ralph Müller-Pfefferkorn, Arne Rümmler, Michael Wagner, Lars Bernard, Ralf Seppelt
Publikováno v:
International Journal of Digital Earth, Vol 16, Iss 1, Pp 1510-1529 (2023)
Geospatial data are fundamental in most global-change and sustainability-related domains. However, readily accessible information on data quality and provenance is often missing or hardly accessible for users due to technical or perceptual barriers,
Externí odkaz:
https://doaj.org/article/9bb9be8e332b4b6db135ac4d8b2399bf
Elucidating electrostatic surface potentials contributes to a deeper understanding of the nature of matter and its physicochemical properties, which is the basis for a wide field of applications. Scanning quantum dot microscopy, a recently developed
Externí odkaz:
http://arxiv.org/abs/2004.02488
Autor:
Boting Ning, Andrew M. Tilston-Lunel, Justice Simonetti, Julia Hicks-Berthet, Adeline Matschulat, Roxana Pfefferkorn, Avrum Spira, Matthew Edwards, Sarah Mazzilli, Marc E. Lenburg, Jennifer E. Beane, Xaralabos Varelas
Publikováno v:
Journal of Experimental & Clinical Cancer Research, Vol 42, Iss 1, Pp 1-18 (2023)
Abstract Background Bronchial premalignant lesions (PMLs) are composed primarily of cells resembling basal epithelial cells of the airways, which through poorly understood mechanisms have the potential to progress to lung squamous cell carcinoma (LUS
Externí odkaz:
https://doaj.org/article/8eeb49dcba4b46ed83d733498621d057