Tuning Guidelines for Model-Predictive Control

Abstract

This paper reviews available tuning guidelines for model-predictive control (MPC) from theoretical and practical perspectives. Its primary focus is on the guidelines introduced since the publication of our previous review of MPC tuning guidelines in this same journal in 2010. Since then, new guidelines based on approaches such as pole placement and multiobjective optimization have been proposed, and more autotuning methods have been introduced. This review covers different implementations of MPC such as dynamic matrix control, generalized predictive control, and state-space-model predictive control that requires Kalman filter tuning. The closed-loop performances of a distillation column and the Shell fractionator under model-predictive controllers tuned using four different tuning guidelines are compared through numerical simulations.