4 citations · 11 across the 9 of their papers we have counts for
9 papers
Branch-Stochastic Model Predictive Control for Motion Planning under Multi-Modal Uncertainty with Scenario Clustering
Zekun Xing, Ramkrishna Chaudhari, Marion Leibold +2
Motion planning for autonomous driving must account for multi-modal uncertainty in both the intentions and trajectories of surrounding vehicles. Handling uncertainty in a worst-cas…
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…
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…
Safe and Non-Conservative Trajectory Planning for Autonomous Driving Handling Unanticipated Behaviors of Traffic Participants
Tommaso Benciolini, Michael Fink, Nehir Güzelkaya +2
Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Mo…
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…
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…
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…
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)…