On the Improvement of Set-point Tracking Performance of Model Predictive Control

Model predictive control (MPC) has been attractive in practical designs due to the inherent manner in which both control and state constraints can be incorporated. An open problem is the choice of an appropriate prediction horizon that guarantees both stability and performance. The goal of this paper is to show that an optimal prediction window arrived at by switching between multiple receding-horizon controllers can provide closed loop stability and improve tracking performance.