  Model Predictive Control (MPC) is the principal control technique used in
industrial applications. Although it offers distinguishable qualities that make
it ideal for industrial applications, it can be questioned its robustness
regarding model uncertainties and external noises. In this paper we propose a
robust MPC controller that merges the simplicity in the design of MPC with
added robustness. In particular, our control system stems from the idea of
adding robustness in the prediction phase of the algorithm through a specific
robust Kalman filter recently introduced. Notably, the overall result is an
algorithm very similar to classic MPC but that also provides the user with the
possibility to tune the robustness of the control. To test the ability of the
controller to deal with errors in modeling, we consider a servomechanism system
characterized by nonlinear dynamics.
