  Many popular approaches in the field of robust model predictive control (MPC)
are 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 are: (i) the design of its main
ingredients are tractable for medium to large-sized systems, (ii) the terminal
set does not need to be robust with respect to all the possible system
uncertainties, but only for a reduced set that can be made arbitrarily small,
thus facilitating its design and implementation, (iii) under certain conditions
the terminal set can be taken as a positive invariant set of the nominal
system, allowing us to use a terminal equality constraint, which facilitates
its application to large-scale systems, and (iv) the complexity of its
optimization problem is comparable to the non-robust MPC variant. We show
numerical closed-loop results of its application to a multivariable chemical
plant and compare it against other robust MPC formulations.
