1 citations · 1 across the 12 of their papers we have counts for
12 papers
Uncertainty-Aware Fusion for Resilient Distributed Radar Sensing
Christian Eckrich, Maik Pfefferkorn, Rolf Findeisen +2
Distributed radar sensing enables safe and resilient operation in mobile robotic and vehicular systems by combining multiple local views of the scene. In this paper, we consider a…
Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Lukas Theiner, Maik Pfefferkorn, Yongpeng Zhao +2
Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using…
High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
Sebastian Hirt, Valentinus Suwanto, Hendrik Alsmeier +2
Learning controller parameters from closed-loop data has been shown to improve closed-loop performance. Bayesian optimization, a widely used black-box and sample-efficient learning…
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…
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Yongpeng Zhao, Maik Pfefferkorn, Maximilian Templer +1
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, makin…
Probabilistically Input-to-State Stable Stochastic Model Predictive Control
Maik Pfefferkorn, Rolf Findeisen
Employing model predictive control to systems with unbounded, stochastic disturbances poses the challenge of guaranteeing safety, i.e., repeated feasibility and stability of the cl…
Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
Sebastian Hirt, Andreas Höhl, Johannes Pohlodek +4
Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitab…
Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control
Sebastian Hirt, Maik Pfefferkorn, Rolf Findeisen
Safe learning of control policies remains challenging, both in optimal control and reinforcement learning. In this article, we consider safe learning of parametrized predictive con…
Stability-informed Bayesian Optimization for MPC Cost Function Learning
Sebastian Hirt, Maik Pfefferkorn, Ali Mesbah +1
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predicti…
Regret and Conservatism of Distributionally Robust Constrained Stochastic Model Predictive Control
Maik Pfefferkorn, Venkatraman Renganathan, Rolf Findeisen
We analyse the conservatism and regret of distributionally robust (DR) stochastic model predictive control (SMPC) when using moment-based ambiguity sets for modeling unknown uncert…
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
Johanna Bethge, Maik Pfefferkorn, Alexander Rose +2
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs…
Fusing Online Gaussian Process-Based Learning and Control for Scanning Quantum Dot Microscopy
Maik Pfefferkorn, Michael Maiworm, Christian Wagner +2
Elucidating electrostatic surface potentials contributes to a deeper understanding of the nature of matter and its physicochemical properties, which is the basis for a wide field o…