文章基本信息

标题：Semi-Active Suspension Control Design via Bayesian Optimization
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作者：Gianluca Savaia ; Simone Formentin ; Sergio M. Savaresi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14312-14317
DOI：10.1016/j.ifacol.2020.12.1374
语种：English
出版社：Elsevier
摘要：AbstractThe fine tuning of semi-active suspension control systems for road vehicles is usually a costly and burdensome task, needing control expertise and many hours of professional driving. In this paper, we propose a data-driven tuning method enabling the automatic calibration of the parameters of the suspension controller using a small number of experiments and exploiting Bayesian Optimization tools. The effectiveness of the proposed approach is validated on a commercial multi-body simulator. As a side contribution, the approach is shown to be robust with respect to variations of the testing conditions.
关键词：KeywordsSemi-active suspensionVehicle dynamicsBayesian optimizationCalibration