PLS-Based Robust Inferential Control for a Packed-Bed Reactor

This paper compares the performance of two different inferential schemes when applied to an experimental packed-bed reactor. The first scheme, proposed initially by Brosilow, is designed based on Kalman filter estimation. The second less traditional design uses an estimator computed from the Partial...

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Bibliographic Details
Published in1991 American Control Conference pp. 256 - 261
Main Authors Budman, H.M., Holcomb, T., Morari, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.1991
Subjects
Chemical engineering
Chemical technology
Electrical equipment industry
Inductors
Kalman filters
Robust control
Robustness
Tellurium
Temperature measurement
Uncertainty
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Summary:This paper compares the performance of two different inferential schemes when applied to an experimental packed-bed reactor. The first scheme, proposed initially by Brosilow, is designed based on Kalman filter estimation. The second less traditional design uses an estimator computed from the Partial Least Squares regression method (PLS). The second approach was found to give superior performance when the nonlinear system under study is operated is a wide range of operating points. Due to the nonlinearity of the system it is essential to address the issue of robustness of the proposed schemes. This is formally done in this work using Structured Singular Value Theory. For the robustness analysis it is crucial to develop a realistic but not overly conservative uncertainty description. Since the PLS estimator uses a large number of measurements, a robust design based on the uncertainty associated with each one of the measurements would be very conservative. To overcome this problem a lumped uncertainty description is proposed which is identified directly from experiments.
ISBN:0879425652
9780879425654
DOI:10.23919/ACC.1991.4791367