Control-relevant curvefitting for plant-friendly multivariable system identification

Hyunjin Lee, Daniel Rivera

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

4 Scopus citations

Abstract

A control-relevant parameter estimation algorithm is developed in this paper for curvefitting Empirical Transfer Function Estimates (ETFEs) with orthogonal (i.e., zippered) frequency grids to discrete-time parametric Matrix Fraction Description models. Such ETFEs arise from DFT analysis of identification data generated from constrained, plant-friendly multisine inputs as developed by the authors' previous work. This curvefitter minimizes model estimation error using pre/post frequency-dependent weighting matrices as functions of the closed-loop dynamics. The control-relevant multivariable parameter estimation procedure is illustrated with an example case study based on the Shell Heavy Oil Fractionator problem.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Pages1431-1436
Number of pages6
Volume2
Publication statusPublished - 2005
Event2005 American Control Conference, ACC - Portland, OR, United States
Duration: Jun 8 2005Jun 10 2005

Other

Other2005 American Control Conference, ACC
CountryUnited States
CityPortland, OR
Period6/8/056/10/05

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ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Lee, H., & Rivera, D. (2005). Control-relevant curvefitting for plant-friendly multivariable system identification. In Proceedings of the American Control Conference (Vol. 2, pp. 1431-1436). [WeC09.1]