Model predictive control is an important model-based control strategy devised for large
multiple-input, multiple-output control problems with inequality constraints on the input
and outputs. Applications typically involve two types of calculations: (1) a steady-state
optimization to determine the optimum set points for the control calculations, and (2)
control calculations to determine the input changes that will drive the process to the set
points. The success of model-based control strategies such as MPC depends strongly on
the availability of a reasonably accurate process model. Consequently, model
development is the most critical step in applying MPC.
As Rawlings (2000) has noted, “feedback can overcome some effects of poor model, but
starting with a poor process model is a kind to driving a car at night without headlight.”
Finally the MPC design should be chosen carefully.
Model predictive control has had a major impact on industrial practice, with over 4500
applications worldwide. MPC has become the method of choice for difficult control
problems in the oil refining and petrochemical industries. However, it is not a panacea
for all difficult control problem(Shinkey, 1994; Hugo, 2000). Furthermore, MPC has had
much less impact in the order process industries. Performance monitoring of MPC
systems is an important topic of current research interest
