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Robustness and Identification Issues in Horizon Predictive Control With Application to a Binary Distillation Column

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5 Author(s)
Rivera, D.E. ; Department of Chemical, Bio and Materials Engineering, Computer-Integrated Manufacturing Systems Research Center, Arizona State University, Tempe, Arizona 85287-6006; Control Systems Engineering Laboratory, Computer-Integrated Manufacturing Systems Research Center, Arizona State University, Tempe, Arizona 85287-6006 ; Jun, K.S. ; Elisante, E. ; Sater, V.E.
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This paper analyzes the robustness properties and modeling requirements for model-predictive control via Horizon Predictive Control (HPC). The theory of Structured Singular Values is used to determine optimal values for the correction horizon in HPC given user-provided uncertainty intervals and performance weights. Regarding system identification, control-relevant identification principles are used to provide guidelines for input signal design, prefiltered estimation, and uncertainty modeling. These results are tested experimentally using data from a methanol-isopropanol distillation column.

Published in:

American Control Conference, 1992

Date of Conference:

24-26 June 1992