23 citations · 119 across the 20 of their papers we have counts for
20 papers
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…
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…
Incorporating Target Vehicle Trajectories Predicted by Deep Learning Into Model Predictive Controlled Vehicles
Ni Dang, Zengjie Zhang, Jizheng Liu +2
Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a fin…
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…
Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning
Ni Dang, Tao Shi, Zengjie Zhang +3
The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying th…
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…
Safe Stochastic Model Predictive Control
Tim Brüdigam, Robert Jacumet, Dirk Wollherr +1
Combining efficient and safe control for safety-critical systems is challenging. Robust methods may be overly conservative, whereas probabilistic controllers require a trade-off be…
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
Zhehua Zhou, Ozgur S. Oguz, Yi Ren +2
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing saf…
Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon
Tim Brüdigam, Daniel Prader, Dirk Wollherr +1
Horizon length and model accuracy are defining factors when designing a Model Predictive Controller. While long horizons and detailed models have a positive effect on control perfo…
Multistage Stochastic Model Predictive Control for Urban Automated Driving
Tommaso Benciolini, Tim Brüdigam, Marion Leibold
Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approac…
Collision Avoidance with Stochastic Model Predictive Control for Systems with a Twofold Uncertainty Structure
Tim Brüdigam, Jie Zhan, Dirk Wollherr +1
Model Predictive Control (MPC) has shown to be a successful method for many applications that require control. Especially in the presence of prediction uncertainty, various types o…
Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing
Tim Brüdigam, Alexandre Capone, Sandra Hirche +2
A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method…
Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems
Zhehua Zhou, Ozgur S. Oguz, Marion Leibold +1
For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning…
Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version
Tim Brüdigam, Michael Olbrich, Dirk Wollherr +1
Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding ve…
Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments
Tim Brüdigam, Fulvio di Luzio, Lucia Pallottino +2
Stochastic Model Predictive Control has proved to be an efficient method to plan trajectories in uncertain environments, e.g., for autonomous vehicles. Chance constraints ensure th…
Minimization of Constraint Violation Probability in Model Predictive Control
Tim Brüdigam, Victor Gaßmann, Dirk Wollherr +1
While Robust Model Predictive Control considers the worst-case system uncertainty, Stochastic Model Predictive Control, using chance constraints, provides less conservative solutio…
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…