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Chance-constrained model predictive control
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DOI：10.1002/aic.690450811.png)
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Abstract
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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.
Keywords:
RECEDING HORIZON CONTROL
STABILITY
SYSTEMS
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