This paper introduces recursive techniques for sprung mass estimation of vehicles on rough terrain. It proposes a base excitation model that treats vertical unsprung accelerations, instead of the terrain profile or a suspension actuator, as the input to the estimation model. The experimental results demonstrate that the proposed method is viable for vehicle sprung mass estimation for vehicles driving on rough (and relatively smooth) terrain. No prior knowledge of the terrain profile is required, and no active/semi-active suspension is required. The PC (Polynomial Chaos) approach is compared with regressor model approaches and filtering approaches. The regressor methods are concluded as not appropriate for this sprung mass estimation approach because of their sensitivity to tuning parameters. The filtering methods are also more difficult to tune than the PC approach, but they can be tuned to converge to nearly the same estimates produced via the PC approach. Based on these results, the PC approach is recommended when sufficient computational resources are available, otherwise the extended Kaiman filter is recommended.


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    Title :

    Vehicle sprung mass estimation for rough terrain


    Contributors:

    Published in:

    Publication date :

    2013


    Size :

    22 Seiten, 14 Bilder, 2 Tabellen, 30 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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    Pence, Benjamin | Online Contents | 2013


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