Controller tuning with Bayesian optimization and its acceleration: Concept and experimental validation

Abstract

This work discusses a data-driven approach to controller parameter tuning based on Bayesian optimization. In particular, we propose to design the prior mean function based on a model of the plant. By encoding the information on the model, the optimization needs a much fewer iterations than standard approaches. The effectiveness of the proposed method is demonstrated with a practical experiment.