5 citations · 13 across the 6 of their papers we have counts for
6 papers
Grasping in Uncertain Environments: A Case Study For Industrial Robotic Recycling
Annalena Daniels, Sebastian Kerz, Salman Bari +2
Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Wa…
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)…