Robust stable model predictive control with constraint tightening

This paper generalizes the constraint tightening approach to robust model predictive control, which guarantees robust feasibility and convergence for a constrained linear system subject to persistent, unknown but bounded disturbances. The constraints in the optimization are tightened in a monotonic sequence such that a predetermined candidate correction policy is feasible for all possible disturbances. The generalization in this paper enables the candidate policy to be time-varying and considers a general convergence problem. A key feature of the generalization is the potential to use a range of nilpotent candidate policies, which eliminate the need to compute a robustly invariant terminal constraint set