Experimental automatic calibration of a semi-active suspension controller via Bayesian Optimization
Gianluca Savaia, Youngil Sohn, Simone Formentin, Giulio Panzani, Matteo Corno, Sergio M. Savaresi
Abstract
The End-of-Line (EoL) calibration of semi-active suspension systems for road vehicles is usually a critical and expensive task, needing a team of vehicle and control experts as well as many hours of professional driving. In this paper, we propose a purely data-based tuning method enabling the automatic calibration of the parameters of a proprietary suspension controller by relying on little experimental time and exploiting Bayesian Optimization tools. A detailed methodology on how to select the most critical degrees of freedom of the algorithm is also provided. The effectiveness of the proposed approach is assessed on a commercial multi-body simulator as well as on a real car.