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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…
eess.SY2017★ 24 cited
On the Design of LQR Kernels for Efficient Controller Learning
Alonso Marco, Philipp Hennig, Stefan Schaal +1
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct cont…