Automated tuning of large-scale multivariable model predictive controllers for spatially-distributed processes

Abstract

An automated tuning method of a large-scale multivariable model predictive controller for multiple array papermaking machine cross-directional (CD) processes can significantly improve the performance of the controller over traditional controllers. Paper machine CD processes are large-scale spatially-distributed dynamical systems. Due to these systems' (almost) spatially invariant nature, the closed-loop transfer functions are approximated by transfer matrices with rectangular circulant matrix blocks, whose input and output singular vectors are the Fourier components of dimension equivalent to either number of actuators or number of measurements.; This approximation enables the model predictive controller for these systems to be tuned by a numerical search over optimization weights in order to shape the closed-loop transfer functions in the two-dimensional frequency domain for performance and robustness. A novel scaling method is used for scaling the inputs and outputs of the multivariable system in the spatial frequency domain.

Description

technical field [0001] The present invention relates generally to techniques for monitoring and controlling continuous sheetmaking systems, and in particular to large-scale multivariate model predictive controllers that automatically adjust for spatially distributed processes, such as paper machine cross processes. Background technique [0002] In the art of making paper with modern high speed machines, paper properties must be continuously monitored and controlled to assure paper quality and to minimize the amount of off-spec finished product in the event of disturbances in the manufacturing process. The paper variables most often measured include basis weight, moisture content, and paper caliper (ie, caliper) of the paper at various stages in the manufacturing process. These process variables are typically controlled by, for example, adjusting the feedstock supply rate at the beginning of the process, adjusting the amount of steam applied to the paper near the middle of th...

AI Technical Summary

Problems solved by technology

However, even with a static optimizer, tuning a multivariate CD predictive controller by trial and error is very difficult, even for an experienced engineer, because there are so many (usually >10) tuning parameters

Another practical problem is that it is not indicated whether the controller is robust and stable to the unavoidable model uncertainty

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