  Lately, Nonlinear Model Predictive Control (NMPC)has been successfully
applied to (semi-) autonomous driving problems and has proven to be a very
promising technique. However, accurate control models for real vehicles could
require costly and time-demanding specific measurements. To address this
problem, the exploitation of system data to complement or derive the prediction
model of the NMPC has been explored, employing learning dynamics approaches
within Learning-based NMPC (LbNMPC). Its application to the automotive field
has focused on discrete grey-box modeling, in which a nominal dynamics model is
enhanced by the data-driven component. In this manuscript, we present an LbNMPC
controller for a real go-kart based on a continuous black-box model of the
accelerations obtained by Gaussian Processes. We show the effectiveness of the
proposed approach by testing the controller on a real go-kart vehicle,
highlighting the approximation steps required to get an exploitable GP model on
a real-time application.
