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eess.SY2021★ 10 cited
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
Alexander von Rohr, Matthias Neumann-Brosig, Sebastian Trimpe
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. While learning-based con…
eess.SY2018★ 106 cited
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Matthias Neumann-Brosig, Alonso Marco, Dieter Schwarzmann +1
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model…