23 citations · 119 across the 20 of their papers we have counts for
5 papers · 1 filter
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…