Increasing signal accuracy of automotive wheel-speed sensors by online learning

The wheel speed is an important signal for modern automotive control systems. The performance of these systems is closely related to the quality of the processed wheel speed. However, due to manufacturing tolerances or corrosion respectively of the sensor gear wheel, the quality of the signal is affected negatively. In this paper a software-based method to compensate for the mechanical inaccuracy of the sensor wheel is introduced. The approach increases the signal accuracy of conventional automotive wheel-speed sensors significantly. Moreover, with the presented method, the demand for manufacturing precision of the sensor wheel is reduced, which in turn cuts down the costs for mass production considerably. The correction is obtained by online learning of a correction factor for each sensor impulse using a fuzzy model for reconstructing the actual wheel-speed and recursive parameter identification for estimating the correction factors. A method to determine the angular position of the gear wheel without a reference impulse is proposed. The introduced approach is assessed by applying it to real world data