  This paper introduces the Feasibility Governor (FG): an add-on unit that
enlarges the region of attraction of Model Predictive Control by manipulating
the reference to ensure that the underlying optimal control problem remains
feasible. The FG is developed for linear systems subject to polyhedral state
and input constraints. Offline computations using polyhedral projection
algorithms are used to construct the feasibility set. Online implementation
relies on the solution of a convex quadratic program that guarantees recursive
feasibility. The closed-loop system is shown to satisfy constraints, achieve
asymptotic stability, and exhibit zero-offset tracking.

Optimization and Control