Multivariable uncertainty estimation based on multi-model output matching

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Boling JM, Haggblom KE, Nystrom RH
Publisher: ELSEVIER SCI LTD
Publication year: 2004
Journal: Journal of Process Control
Journal acronym: J PROCESS CONTR
Volume number: 14
Issue number: 3
Start page: 293
End page: 304
Number of pages: 12
ISSN: 0959-1524


Abstract

This paper describes a procedure for deriving norm-bounded output-multiplicative uncertainty descriptions for a multi-input multi-output system by matching the output of an uncertainty model to the outputs of a set of known models. It is assumed that the set of models has been obtained through system identification. The objective is to determine the least conservative uncertainty description such that all known experimental data can be reconstructed by the uncertainty model. Both unstructured and diagonal uncertainty are considered as well as various structures of the uncertainty weight matrix. For the case with no a priori information, it is shown that a nonconservative uncertainty description can be obtained by minimizing the magnitude of the determinant of the uncertainty weight matrix subject to the output-matching condition. The procedure is illustrated by estimation of uncertainty weights and design of mu-optimal controllers for a distillation column. (C) 2003 Elsevier Ltd. All rights reserved.


Keywords

distillation control, model validation, multiple models, robust control, uncertainty estimation

Last updated on 2019-14-10 at 04:41