10 citations · 21 across the 9 of their papers we have counts for
9 papers
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2
Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statistical machine learning, partic…
Active Fault Identification and Robust Control for Unknown Bounded Faults via Volume-Based Costs
Annalena Daniels, Johannes Teutsch, Fabian Kleindienst +2
This paper proposes a novel framework for active fault diagnosis and parameter estimation in linear systems operating in closed-loop, subject to unknown but bounded faults. The app…
Towards safe Bayesian optimization with Wiener kernel regression
Oleksii Molodchyk, Johannes Teutsch, Timm Faulwasser
Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, t…
Adaptive Stochastic Predictive Control from Noisy Data: A Sampling-based Approach
Johannes Teutsch, Christopher Narr, Sebastian Kerz +2
In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive contro…
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…
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…
Offline Uncertainty Sampling in Data-driven Stochastic MPC
Johannes Teutsch, Sebastian Kerz, Tim Brüdigam +2
In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The…
Data-driven tube-based stochastic predictive control
Sebastian Kerz, Johannes Teutsch, Tim Brüdigam +2
A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time invariant (LTI)…
Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity
Tim Brüdigam, Johannes Teutsch, Dirk Wollherr +1
Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive C…