  In this work, a novel approach is presented to solve the problem of tracking
trajectories in autonomous vehicles. This approach is based on the use of a
cascade control where the external loop solves the position control using a
novel Takagi Sugeno - Model Predictive Control (TS-MPC) approach and the
internal loop is in charge of the dynamic control of the vehicle using a Takagi
Sugeno - Linear Quadratic Regulator technique designed via Linear Matrix
Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the
kinematic and dynamic models of the vehicle. In addition, a novel Takagi Sugeno
estimator - Moving Horizon Estimator - Unknown Input Observer (TS-MHE-UIO) is
presented. This method estimates the dynamic states of the vehicle optimally as
well as the force of friction acting on the vehicle that is used to reduce the
control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO
techniques is that using the TS model formulation of the vehicle allows us to
solve the nonlinear problem as if it were linear, reducing computation times by
40-50 times. To demonstrate the potential of the TS-MPC we propose a comparison
between three methods of solving the kinematic control problem: using the
non-linear MPC formulation (NL-MPC), using TS-MPC without updating the
prediction model and using updated TS-MPC with the references of the planner.
