Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
This provides a data-efficient solution for optimizing industrial control systems, though it is incremental as it applies an existing method to a new domain.
The paper tackles the problem of automatically tuning controller parameters in industrial systems using Bayesian optimization, achieving better performance than manual calibration with fewer experiments.
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.