Controller tuning with Bayesian optimization and its acceleration: Concept and experimental validation. Issue 3 (9th May 2022) - Fujimoto, Yusuke; Sato, Hiroki; Nagahara, Masaaki
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.