  In this paper we propose a robust Model Predictive Control where a Gated
Recurrent Unit network model is used to learn the input-output dynamic of the
system under control. Robust satisfaction of input and output constraints and
recursive feasibility in presence of model uncertainties are achieved using a
constraint tightening approach. Moreover, new terminal cost and terminal set
are introduced in the Model Predictive Control formulation to guarantee
Input-to-State Stability of the closed loop system with respect to the
uncertainty term.
