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  • 标题:Friction State Classification Based on Vehicle Inertial Measurements
  • 本地全文:下载
  • 作者:Donald Selmanaj ; Matteo Corno ; Sergio M. Savaresi
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:5
  • 页码:72-77
  • DOI:10.1016/j.ifacol.2019.09.012
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractTire-road friction is the most important characteristic defining the planar dynamics of wheeled vehicles. It has consequences on the drivability, stability and tuning of the active vehicle dynamics control systems. This paper proposes two online friction estimation methods designed for the adaptation of vehicle dynamics control algorithms. The problem is framed as a classification problem where inertial measurements are used to discriminate between high and low friction regimes. The first method merges a recursive least-squares (RLS) algorithm with a heuristic bistable logic to classify the friction condition and promptly react to its changes. The second method runs a classification algorithm on the slip-acceleration characteristic. Both methods simultaneously account for the longitudinal and lateral dynamics and are tested on experimental data.
  • 关键词:KeywordsFrictionVehicles dynamicsClassificationRecursive algorithmsNonlinear algorithms
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