Title: Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study,
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.