The purpose of this study is to estimate the vehicle mass for vehicle safety control. The vehicle mass is considered to be a constant parameter for some vehicle safety control systems, but it changes according to the number of the passengers or the load weight that the vehicle carries. This paper suggests an integrated vehicle mass estimation algorithm using the recursive least-squares method and adaptation laws. First, the vehicle mass is estimated from the longitudinal dynamics using the recursive least-squares method. Second, three kinds of estimation algorithm are suggested from the roll dynamics. Two of the algorithms are designed using the adaptation law from a Lyapunov stability analysis and the roll angle observer, and the last algorithm is designed using the recursive least-squares method. Finally, the multiple-observer synthesis integrates the estimated mass values calculated using the longitudinal dynamics and the roll dynamics. The proposed vehicle mass estimation algorithm is evaluated via simulation using CarSim and via experimentation using a test vehicle.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Integrated vehicle mass estimation for vehicle safety control using the recursive least-squares method and adaptation laws




    Publication date :

    2015




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    RVK:    ZO 4200 / ZO 4200:
    Local classification TIB:    275/7025
    BKL:    55.20 Straßenfahrzeugtechnik / 55.20






    ONLINE ESTIMATION OF VEHICLE DRIVING RESISTANCE PARAMETERS WITH RECURSIVE LEAST SQUARES AND RECURSIVE TOTAL LEAST SQUARES

    Rhode, S. / Gauterin, F. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2013


    Robust Vehicle Mass Estimation Using Recursive Least M-Squares Algorithm for Intelligent Vehicles

    Chor, Wai Tong / Tan, Chee Pin / Bakibillah, A. S. M. et al. | IEEE | 2024