Performance-optimized identification of cross-directional control processes

Examines high-performance practical algorithms for identification of cross-directional processes from input/output data. Instead of working directly with the original two-dimensional array of the high-resolution profile scans, the proposed algorithms use separation properties of the problem. It is d...

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Bibliographic Details
Published inProceedings of the 36th IEEE Conference on Decision and Control Vol. 2; pp. 1872 - 1877 vol.2
Main Authors Gorinevsky, D., Heaven, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Actuators
Control systems
Electrical equipment industry
Industrial control
Manufacturing industries
Manufacturing processes
Process control
Pulp manufacturing
Time series analysis
Transfer functions
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Summary:Examines high-performance practical algorithms for identification of cross-directional processes from input/output data. Instead of working directly with the original two-dimensional array of the high-resolution profile scans, the proposed algorithms use separation properties of the problem. It is demonstrated that by estimating and identifying in turn cross directional and time responses of the process, it is possible to obtain unbiased least-square error estimates of the model parameters. At each step, a single data sequence is used for identification which ensures high computational performance of the proposed algorithm. A theoretical proof of algorithm convergence is presented. The discussed algorithms are implemented in an industrial identification tool and the paper includes real-life examples using paper machine data.
ISBN:0780341872
9780780341876
ISSN:0191-2216
DOI:10.1109/CDC.1997.657857