Efficient algorithms for trading off feasibility and performance in predictive control

Abstract

This article introduces two simple modifications to conventional predictive control algorithms with the aim of enlarging the feasible regions without increasing computational complexity. It is shown that despite the relatively large feasibility gains, the loss in performance may be far smaller than expected and thus the algorithms give mechanisms for achieving low computational loads with good feasibility and good performance while using a simple algorithm set-up. Both algorithms have standard convergence and feasibility guarantees.