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Found 2 papers, 1 papers with code
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design.
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data.
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