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20202024
most citedGaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing

17 citations · 36 across the 10 of their papers we have counts for

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eess.SY20244 cited

Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems

Michael Fink, Tim Brüdigam, Dirk Wollherr +1

Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the co…

eess.SY2024

Combining Belief Function Theory and Stochastic Model Predictive Control for Multi-Modal Uncertainty in Autonomous Driving

Tommaso Benciolini, Yuntian Yan, Dirk Wollherr +1

In automated driving, predicting and accommodating the uncertain future motion of other traffic participants is challenging, especially in unstructured environments in which the hi…

eess.SY2024

Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty - Extended Version

Johannes Teutsch, Sebastian Kerz, Dirk Wollherr +1

We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach u…

eess.SY2023

Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

Tommaso Benciolini, Chen Tang, Marion Leibold +3

Autonomous racing creates challenging control problems, but Model Predictive Control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-hea…

eess.SY20231 cited

Optimal Control for Indoor Vertical Farms Based on Crop Growth

Annalena Daniels, Michael Fink, Marion Leibold +2

Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security. This closed and highly controlled system…

eess.SY2023

An Online Adaptation Strategy for Direct Data-driven Control

Johannes Teutsch, Sebastian Ellmaier, Sebastian Kerz +2

The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unkn…