This paper studies the behavior of a new recursive least-squares (RLS) adaptive algorithm for automotive suspension system identification. RLS algorithms tend to increase the identification error when the input power is low. Like the Variable Memory Length algorithm (VML) [10], the new algorithm, called Robust VML (RVML), is robust in system identification applications in which the input power is significantly reduced during operation. However, RVML is more robust then VML to variations in input power. Simulations with input signals generated according to models that represent the road profile show that the RVML algorithm has the same convergence speed as both the VML and the VFF [6] algorithms, the latter being the one of the most referenced ones for automotive suspension system identification. In steady-state, the RVML algorithm outperforms the other algorithms for any condition of input power. It should encounter application in automotive suspension fault detection systems and in adaptive control for semi-active suspension systems. In both cases, considerable periods of input power variation during operation are common.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Robust recursive least squares algorithm for automotive suspension identification


    Additional title:

    Sae Technical Papers



    Conference:

    SAE Brasil 2005 Congress and Exhibit ; 2005



    Publication date :

    2005-11-22




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    A Dual-Recursive-Least-Squares Algorithm for Automotive Radar Interference Suppression

    Wang, Ping / Yin, Xuefeng / Rodriguez-Pineiro, Jose et al. | IEEE | 2023


    A recursive least squares algorithm robust to low-power excitation

    Ludovico, C.S. / Bermudez, J.C.M. | Tema Archive | 2004


    Recursive Least-Squares Filtering

    Zarchan, Paul / Musoff, Howard | AIAA | 2015


    Recursive Least-Squares Filtering

    Musoff, Howard / Zarchan, Paul | AIAA | 2009


    Recursive Least-Squares Filtering

    Musoff, Howard / Zarchan, Paul | AIAA | 2005