Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control

We propose an adaptive nonlinear model predictive control (NMPC) for vehicle tracking control. The controller learns in real time a tire force model to adapt to a varying road surface that is only indirectly observed from the effects of the tire forces determining the vehicle dynamics. Learning the entire tire model from data would require driving in the unstable region of the vehicle dynamics with a prediction model that has not yet converged. Instead, our approach combines NMPC with a noise-adaptive particle filter for vehicle state and tire stiffness estimation and a pre-determined library of tire models. The stiffness estimator determines the linear component of the tire model during normal vehicle driving, and the control strategy exploits a relation between the tire stiffness and the nonlinear part of the tire force to select the appropriate full tire model from the library, which is then used in the NMPC prediction model. We validate the approach in simulation using real vehicle parameters, demonstrate the real-time feasibility in automotive-grade processors using a rapid prototyping unit, and report preliminary results of experimental validation on a snow-covered test track.