Tire-road friction forces are an important area in vehicle dynamics research since the motion of a road vehicle is primarily affected by the friction forces transferred from the road through four small contact areas, the tire footprints. Therefore, most of the vehicle control systems both classical, e.g. ABS and ESP, and even in more recent applications, e.g. collision avoidance, adaptive cruise control, automated lane-keeping system, take these forces into account for algorithm developments. However, it is usually complicated or costly to measure friction forces directly. Thus, an indirect measurement based on a model-based estimation approach that can eliminate the above constraints is applied. The tire-road friction forces are estimated from easily measurable signals. The obtained estimated friction forces can be used further in vehicle control systems and made the algorithms more road-adaptive. This paper proposes the method of tire-road friction forces estimation using Extended Kalman Filtering (EKF) and Unscented Kalman Filtering (UKF). Each friction force is modeled as a first-order Gauss-Markov (GM) stochastic process. They are added to a seven degree-of-freedom vehicle model. The EKF or UKF algorithm can then be applied as an estimator yielding the estimated friction forces. Simulation results for both cases as well as a validation with field-test data are also presented.


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    Title :

    Estimation of tire-road friction forces using extended and unscented Kalman filtering for advanced vehicle control


    Contributors:


    Publication date :

    2008


    Size :

    9 Seiten, 20 Bilder, 1 Tabelle, 16 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English