Robustness and identification issues in Horizon Predictive Control with application to a binary distillation column

Daniel Rivera, K. S. Jun, E. Elisante, V. E. Sater, B. C. Horn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherPubl by American Automatic Control Council
Pages3031-3036
Number of pages6
ISBN (Print)0780302109, 9780780302105
DOIs
StatePublished - 1992
EventProceedings of the 1992 American Control Conference - Chicago, IL, USA
Duration: Jun 24 1992Jun 26 1992

Publication series

NameProceedings of the American Control Conference
Volume4
ISSN (Print)0743-1619

Other

OtherProceedings of the 1992 American Control Conference
CityChicago, IL, USA
Period6/24/926/26/92

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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