4 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Real-time Gaussian Process based Approximate Model Predictive Trajectory Tracking Control for Autonomous Vehicles
Alexander Rose, Lukas Theiner, Rolf Findeisen
Applying model predictive control on embedded systems remains challenging due to the high computational cost of solving optimal control problems. To address this limitation, comput…
A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Control
Sebastian Hirt, Lukas Theiner, Maik Pfefferkorn +1
Many control problems require repeated tuning and adaptation of controllers across distinct closed-loop tasks, where data efficiency and adaptability are critical. We propose a hie…
Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles
Lukas Theiner, Sebastian Hirt, Alexander Steinke +1
Trajectory planning for automated vehicles commonly employs optimization over a moving horizon - Model Predictive Control - where the cost function critically influences the result…
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification
Hendrik Alsmeier, Lukas Theiner, Anton Savchenko +2
This paper presents a framework for bounding the approximation error in imitation model predictive controllers utilizing neural networks. Leveraging the Lipschitz properties of the…
Time-Series-Informed Closed-loop Learning for Sequential Decision Making and Control
Sebastian Hirt, Lukas Theiner, Rolf Findeisen
Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the choice of controller parameters. Bayesian optimization a…