Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model

In this paper we propose a robust Model Predictive Control where a Gated Recurrent Unit network model is used to learn the input–output dynamics 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.