8 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
Real-Time Non-Smooth MPC for Switching Systems: Application to a Three-Tank Process
Hendrik Alsmeier, Felix Häusser, Andreas Knödler +4
Real-time model predictive control of non-smooth switching systems remains challenging due to discontinuities and the presence of discrete modes, which complicate numerical integra…
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…
Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
Hendrik Alsmeier, Anton Savchenko, Rolf Findeisen
The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predicti…
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks
J. Pohlodek, H. Alsmeier, B. Morabito +3
Integrating measurements and historical data can enhance control systems through learning-based techniques, but ensuring performance and safety is challenging. Robust model predict…