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.