Real-time estimation of the vehicle sideslip angle through regression based on principal component analysis and neural networks

Accurate estimation of the vehicle sideslip angle is fundamental in vehicle dynamics control and stability. In this paper two different methods for vehicle sideslip estimation, based on Principal Component Analysis (PCA) and Neural Networks (NN), are presented comparing the procedure responses with full-scale vehicle acquired test data. The estimation algorithms use driver's steering angle, lateral and longitudinal accelerations, wheel angular velocities and yaw rate measured from sensors integrated in a test vehicle, and are validated by comparison with the measurements of the sideslip angle provided by an optical Correvit sensor suitably mounted on board, serving as the reference system in terms of accuracy of slip-free measurement of longitudinal and transverse vehicle dynamics. The procedure results, based on both the original (RAW) and the reduced (PCA) data sets, are compared to the acquired sideslip angle, using the estimated channel as an input for the TRICK tool to evaluate the accuracy of the results and the potential of the estimation process in terms of tire interaction curves.