Chance Constrained Model Predictive Control
نویسندگان
چکیده
This work focuses on robustness of model predictive control (MPC) 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.
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