23 citations · 62 across the 11 of their papers we have counts for
11 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…
Online Constraint Tightening in Stochastic Model Predictive Control: A Regression Approach
Alexandre Capone, Tim Brüdigam, Sandra Hirche
Solving chance-constrained stochastic optimal control problems is a significant challenge in control. This is because no analytical solutions exist for up to a handful of special c…
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-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)…
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…
(Stochastic) Model Predictive Control -- a Simulation Example
Tim Brüdigam
This brief introduction to Model Predictive Control specifically addresses stochastic Model Predictive Control, where probabilistic constraints are considered. A simple linear syst…
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…