Vehicle lateral stability control via approximated NMPC: real-time implementation and software-in-the-loop test

A Nonlinear Model Predictive Control (NMPC) approach to improve vehicle yaw stability and handling by means of a rear active differential is introduced. In particular, the use of NMPC is adopted to show its effectiveness in the vehicle stability control context. However, the application of NMPC for automotive control is limited by the required computational load, which is often incompatible with the employed sampling time. In order to allow the on-line implementation of the designed predictive control law, an efficient technique based on Set Membership approximation methodologies using a Nearest Point approach is adopted. The designed efficient NMPC approximation is implemented on an embedded device with limited computational capacity and tested using software-in-the-loop simulations and an accurate nonlinear model of the vehicle. Enhancements on stability in demanding conditions such as mu-split braking and damping properties in impulsive maneuvers are obtained, as well as improvements over a well-assessed approach based on an an enhanced Internal Model Control structure.