Vehicle sideslip estimator using load sensing bearings

Journal article (2016)

Authors

A. Kunnappillil Madhusudhanan Dynamics of Micro and Nano Systems - Mechanical, Maritime and Materials Engineering , TNO
M. Corno Politecnico di Milano
E.G.M. Holweg OLD Intelligent Vehicles & Cognitive Robotics , SKF
Research Group
OLD Intelligent Vehicles & Cognitive Robotics
Kalman filter State estimation Load sensing bearing Observability Vehicle sideslip
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Published Date

2016

Language

English

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Research Group

OLD Intelligent Vehicles & Cognitive Robotics

Abstract

This paper investigates the potential of load based vehicle sideslip estimation. Different techniques to measure tyre forces have been presented over the years; so far no technique has made it to the market. This paper considers a new technology based on load sensing bearings, which provides tyre force measurements. Based on the features of the sensor, a vehicle sideslip angle estimator is designed, analyzed and tested. The paper shows that direct tyre force sensing has mainly two advantages over traditional model-based estimators: primarily, it avoids the use of tyre models, which are heavily affected by uncertainties and modeling errors and secondarily, providing measurements on the road plane, it is less prone to errors introduced by roll and pitch dynamics. Extensive simulation tests along with a detailed analysis of experimental tests performed on an instrumented vehicle prove that the load based estimation outperforms the kinematic model-based benchmark yielding a root mean square error of 0.15°.