Wheel speed measurements provided by incremental encoders in road vehicles are usually affected by a significant periodic noise. Unavoidable geometrical or misalignment errors in the structure of the encoder are here regarded as possible causes of the measurement disturbance. Such disturbances are commonly rejected using simple solutions, like low-pass or notch filters. However, such methods may not be adequate in some applications, as the signal information is canceled jointly with the disturbances, thus jeopardizing the overall system performance. This paper presents an online filtering procedure, based on the geometrical model of the sensor and recursive constrained least squares estimation, aimed at rejecting only the periodic noise. Such a procedure will result into a speed measurement processing that is most suited for advanced vehicle applications. Experimental data are used to show the effectiveness of the proposed approach considering two different vehicles: a bicycle - where the proposed method is shown to be effective for cycling cadence estimation - and a sport car - where the speed variable is of primary importance, e.g., for braking and stability control.

On-line model-based wheel speed filtering for geometrical error compensation

Rallo G.;Formentin S.;Savaresi S. M.
2018-01-01

Abstract

Wheel speed measurements provided by incremental encoders in road vehicles are usually affected by a significant periodic noise. Unavoidable geometrical or misalignment errors in the structure of the encoder are here regarded as possible causes of the measurement disturbance. Such disturbances are commonly rejected using simple solutions, like low-pass or notch filters. However, such methods may not be adequate in some applications, as the signal information is canceled jointly with the disturbances, thus jeopardizing the overall system performance. This paper presents an online filtering procedure, based on the geometrical model of the sensor and recursive constrained least squares estimation, aimed at rejecting only the periodic noise. Such a procedure will result into a speed measurement processing that is most suited for advanced vehicle applications. Experimental data are used to show the effectiveness of the proposed approach considering two different vehicles: a bicycle - where the proposed method is shown to be effective for cycling cadence estimation - and a sport car - where the speed variable is of primary importance, e.g., for braking and stability control.
Bicycles; Filtering; Signal processing; Wheel speed
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1121588
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