Chance-constrained model predictive control

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