The control problem was formulated in the previous chapters considering all signals to
possess an unlimited range. This is not very realistic because in practice all processes are …

Model Predictive Control is an important technique used in the process control industries. It
has developed considerably in the last few years, because it is the most general way of …

The practicality of model predictive control (MPC) is partially limited by the ability to solve
optimization problems in real time. This requirement limits the viability of MPC as a control …

Model predictive control is a form of control in which the current control action is obtained by
solving, at each sampling instant, a finite horizon open-loop optimal control problem, using …

Model predictive control is a form of control in which the current control action is obtained by
solving, at each sampling instant, a finite horizon open-loop optimal control problem, using …

Studies on the theory of model predictive control include the assumption that the origin is in
the interior of the feasible region (that is, the inequality constraints are not active at steady …

The problem of infeasibility caused by inconsistent hard state constraints in model predictive
control is considered. The difficulties that may arise are analyzed, and the inherent trade‐off …

Motivated by requirements in the process industries, the largest user of model predictive
control, we re-examine some features of recent research on this topic. We suggest that some …

Abstract Model predictive control was emerging into the process industries during the
1980's. Today it is commonplace and, in some sense, mature. Commercial packages are …

Describing the principles and applications of single input, single output and multivariable
predictive control in a simple and lively manner, this practical book discusses topics such as …