Journal of South China University of Technology(Natural Science Edition) ›› 2012, Vol. 40 ›› Issue (12): 105-110.

• Automotive Engineering • Previous Articles     Next Articles

Serial RLS-Based Dual-Parameter Combined Identification for Vehicles

Lin Fen  Huang Chao  Wang Wei   

  1. Department of Automotive Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,Jiangsu,China
  • Received:2012-08-22 Revised:2012-09-29 Online:2012-12-25 Published:2012-11-02
  • Contact: 林棻(1980-) ,男,博士,副教授,主要从事汽车动力学与控制研究. E-mail:nhlf2008@yahoo.com.cn
  • About author:林棻(1980-) ,男,博士,副教授,主要从事汽车动力学与控制研究.
  • Supported by:

    国家自然科学基金资助项目( 10902049) ; 中国博士后科学基金资助项目( 2012M521073)

Abstract:

Gross mass and centroid position are two important structural parameters of vehicles that are necessary to be measured in experiments and are essential to the proper operation of vehicle's active safety control system. As the gross mass frequently changes in practice,a dual-parameter combined identification method for vehicles is proposed. This method,which is based on two serial recursive least squares ( RLS) procedures,uses the original vehicle parameters as the initial parameters of the serial RLS-based identification algorithm and identifies the centroid position with the combination of the pylon course slalom test. Then,the identified centroid position is applied to the identification of vehicle mass with the combination of the double-lane change test. Moreover,by taking the variance of the identified vehicle mass sequence as the threshold and by performing limited recursive circulation,the relative errors of the vehicle mass and the distance from the centroid to the front axle both converge to less than 3%. The
effectiveness of the proposed algorithm is finally verified by the virtual test in ADAMS.

Key words: vehicle engineering, parameter identification, recursive least squares method, serial