  This paper continues in the work from arXiv:1903.06103 [math.OC] where a
nonlinear vehicle model was approximated in a purely data-driven manner by a
linear predictor of higher order, namely the Koopman operator. The vehicle
system typically features a lot of nonlinearities such as rigid-body dynamics,
coordinate system transformations and most importantly the tire. These
nonlinearities are approximated in a predefined subset of the state-space by
the linear Koopman operator and used for a linear Model Predictive Control
(MPC) design in the high-dimension state space where the nonlinear system
dynamics evolve linearly. The result is a nonlinear MPC designed by linear
methodologies. It is demonstrated that the Koopman-based controller is able to
recover from a very unusual state of the vehicle where all the aforementioned
nonlinearities are dominant. The controller is compared with a controller based
on a classic local linearization and shortcomings of this approach are
discussed.
