Neuro-Fuzzy Approach to Real Time Total Velocity Vector Estimation of a Passenger Car Covering Critical Situations

Abstract This paper presents the concept of a neuro-fuzzy estimation of the variables of complex, fast, closed-loop systems. It was used to develop an original real time total velocity vector estimator for FWD cars, that covers highly critical driving situations and avoids the use of expensive optical cross-correlation sensors. The aim of this application is to provide vehicle monitoring processes or supervisors, with a reliable value of the longitudinal and transverse velocity components. Fuzzy logic was used to build aggregate indicators with the measured variables, in order to identify and detect the different ways a vehicle behaves. Considering these different typical vehicle behaviours and the complexity of tyre modelling, neural networks were chosen for the estimate of the transverse velocity. A production vehicle was used to validate the implementation of the estimation algorithm.