AN ESTIMATOR BASED ON A SIGMA-POINTS KALMAN FILTER FOR VEHICLE AND TIRE-ROAD CONDITION MONITORING

In the automotive field, the estimation of key variables related to vehicle dynamics, such as the sideslip angle coupled with characteristic tire-road parameters, is fundamental for improving the safety performance of vehicle control systems. Typically, expensive tests are made to characterize tire parameters included in models functional for estimating tire-road forces. A nonlinear model-based estimator, based on the Central Difference Kalman Filter, is proposed in this work for making the vehicle condition monitoring through the coupled estimation of the sideslip angle, the tire-road friction coefficient and lateral forces. An estimator design model is designed around the single-track vehicle model coupled with a parametric estimation strategy functional for estimating tire-road features without specific modelling of tires. The proposed technique is assessed by comparing the estimated data with the ones obtained from a Multibody vehicle model. Two different tests are made considering both non-noisy and noisy measurements.