This paper details an approach to implement closed-loop identification in a model predictive control framework for the cross-direction control of basis weight. The closed-loop identification technique uses the concepts of Markov parameters to determine a step response model that can then be used within a model predictive controller. The technique is applied to an industrial scale paper machine simulation benchmark problem for cases of varying degrees of shrinkage. Performance of the wet and dry end full array sensors are compared. Also the performance of the closed-loop identification is compared for a nominal case and two cases in which shrinkage occurs in the drying process.
Efficient formulations of linear programming based model predictive control are considered for cross direction (CD) control of a dual headbox paper machine. A number of techniques including coincidence points, efficient mathematics, implementation strategies, and the solution of progressively more complicated optimization problems are considered for both their performance and efficiency.
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