  One of the more popular approaches in the field of robust model predictive
control are the ones based on nominal predictions. This paper presents a novel
formulation of this class of controller with proven input-to-state stability
and robust constraint satisfaction. Its advantages, with respect to previous
formulations, are (i) the design of its main ingredients are tractable for
medium to large-sized systems, and (ii) the resulting optimization problem is
simpler than the ones obtained in other formulations. Furthermore, under
certain conditions, the terminal constraint can be eliminated, which
facilitates its application to large-scale systems. We provide procedures for
the design of the main ingredients of the proposed formulation, as well as
discuss some practical considerations for its implementation. Finally, we show
numerical closed-loop results of its application to a multivariable chemical
plant.
