  This paper proposes an Adaptive Robust Model Predictive Control strategy for
lateral control in lane keeping problems, where we continuously learn an
unknown, but constant steering angle offset present in the steering system.
Longitudinal velocity is assumed constant. The goal is to minimize the outputs,
which are distance from lane center line and the steady state heading angle
error, while satisfying respective safety constraints. We do not assume perfect
knowledge of the vehicle lateral dynamics model and estimate and adapt in
real-time the maximum possible bound of the steering angle offset from data
using a robust Set Membership Method based approach. Our approach is even
well-suited for scenarios with sharp curvatures on high speed, where obtaining
a precise model bias for constrained control is difficult, but learning from
data can be helpful. We ensure persistent feasibility using a switching
strategy during change of lane curvature. The proposed methodology is general
and can be applied to more complex vehicle dynamics problems.
