  In this paper, the Model Predictive Control (MPC) and Moving Horizon
Estimator (MHE) strategies using a data-driven approach to learn a
Takagi-Sugeno (TS) representation of the vehicle dynamics are proposed to solve
autonomous driving control problems in real-time. To address the TS modeling,
we use the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to obtain a
set of polytopic-based linear representations as well as a set of membership
functions relating in a non-linear way the different linear subsystems. The
proposed control approach is provided by racing-based references of an external
planner and estimations from the MHE offering a high driving performance in
racing mode. The control-estimation scheme is tested in a simulated racing
environment to show the potential of the presented approaches.
