@article{NeuMarSchTri18,
  title = {Data-efficient Autotuning with Bayesian Optimization: An Industrial Control Study},
  journal = {IEEE Transactions on Control Systems Technology},
  abstract = {Bayesian optimization is proposed for automatic
  learning of optimal controller parameters from experimental
  data. A probabilistic description (a Gaussian process) is used
  to model the unknown function from controller parameters to
  a user-defined cost. The probabilistic model is updated with
  data, which is obtained by testing a set of parameters on the
  physical system and evaluating the cost. In order to learn fast,
  the Bayesian optimization algorithm selects the next parameters
  to evaluate in a systematic way, for example, by maximizing
  information gain about the optimum. The algorithm thus iteratively
  finds the globally optimal parameters with only few
  experiments. Taking throttle valve control as a representative
  industrial control example, the proposed auto-tuning method is
  shown to outperform manual calibration: it consistently achieves
  better performance with a low number of experiments. The
  proposed auto-tuning framework is flexible and can handle
  different control structures and objectives.},
  volume = {28},
  number = {3},
  pages = {730--740},
  month = may,
  year = {2020},
  slug = {bayesianindustrial2018},
  author = {Neumann-Brosig, Matthias and Marco, Alonso and Schwarzmann, Dieter and Trimpe, Sebastian},
  month_numeric = {5}
}