Experimental automatic calibration of a semi-active suspension controller via Bayesian Optimization. (July 2021)

• Record Type: Journal Article
• Authors: Savaia, Gianluca; Sohn, Youngil; Formentin, Simone; Panzani, Giulio; Corno, Matteo; Savaresi, Sergio M.
• Is Part Of: Control engineering practice. Volume 112(2021)
• Publication Date: 2021-07
• Subjects: Semi-active suspensions -- Design of experiments -- Bayesian Optimization -- Calibration; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104826
• Languages: English
• ISSNs: 0967-0661

Abstract

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. Highlights: The calibration of a suspension control system is a burdensome task. Usually, it is performed manually by a calibration engineer. Bayesian Optimization can help automating this process. Self-calibration is experimentally shown to be effective.