Abstract
KEYWORDS – State Estimator, Adhesion Coefficient, Fuzzy Logic, ABS, Artificial Intelligence
ABSTRACT
Tire-road friction characteristics are essential elements of contemporary active safety systems and vehicle dynamics controls, such as anti-lock braking system (ABS) or stability control systems (SCS). The scope of this work is to devise a data fusion method to combine three different methods for estimating the adhesion coefficient between the tire and the road. In this attempt three methods have been utilised; the kalman filter, the magic formula and an analytic model. Fuzzy logic is employed to blend those signals together dynamically adjusting the weight gains between the three different estimation methods. Several validation and verification tests have been performed in CAE environment with correlated vehicle models. Extensive experimental tests have confirmed the initial assumptions and the principles in simulation environment. The benefit of data fusion method is that it can be expanded and easily add new signals that optimise the estimation error either using sensors or incremental estimation techniques. However, this method has to be optimised to be executed even faster and to reduce the computational power that fuzzy logic needs. Overall, this paper shows a data fusion methodology that combines three different estimation methods and provides very good correlation with experimental data.