Chance‐constrained model predictive control

Abstract This work focuses on robustness of model‐predictive control with respect to satisfaction of process output constraints. A method of improving such robustness is presented. The method relies on formulating output constraints as chance constraints using the uncertainty description of the process model. The resulting on‐line optimization problem is convex. The proposed approach is illustrated through a simulation case study on a high‐purity distillation column. Suggestions for further improvements are made.