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  • 标题:Semi-Active Suspension Control Design via Bayesian Optimization
  • 本地全文:下载
  • 作者:Gianluca Savaia ; Simone Formentin ; Sergio M. Savaresi
  • 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
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