Feasibility and Stability of a Kind of Model Predictive Control with Additive Uncertainties

Abstract

In this paper the feasibility and stability of open-loop min-max model predictive control (OL-MMMPC) for systems with additive bounded uncertainties are considered. It is found that the OL-MMMPC may be infeasible and unstable if it is initially feasible. A sufficient condition for feasibility and stability of the OL-MMMPC is presented. Then an improved OL-MMMPC algorithm is proposed, which guarantees the robust stability of the closed-loop system once it is initially feasible. The effectiveness of this algorithm is illustrated by a simulation example.