A model predictive control approach to vehicle yaw control using identified models

Abstract

A vehicle equipped with a front steer-by-wire device is considered and the related control design problem dealt with by using a yaw rate feedback structure. In order to effectively handle both the system non-linearities and the input constraints, a Non-linear Model Predictive Control (NMPC) technique is adopted. A novelty of the present paper is that the vehicle model employed by the NMPC algorithm is obtained from previously collected input/output data, using a Non-linear Set Membership (NSM) identification methodology. Since the NSM approach is able to provide a model with minimal worst-case identification error, improved robustness of the closed-loop system is obtained with respect to that of an NMPC law based on a physical vehicle model. Furthermore, the measure of the model uncertainty provided by the NSM approach allows one to perform a theoretical robust stability analysis of the closed-loop system. The effectiveness of the proposed technique is shown through numerical simulations of manoeuvres using a detailed vehicle model.