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Quantification of model uncertainty from data
Author(s) -
de Vries Douwe K.,
Van Hof Paul M. J. Den
Publication year - 1994
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4590040206
Subject(s) - nonparametric statistics , transfer function , identification (biology) , sensitivity analysis , etfe , interpolation (computer graphics) , errors in variables models , system identification , computer science , measurement uncertainty , function (biology) , robust control , sequence (biology) , control theory (sociology) , uncertainty analysis , mathematics , data modeling , control (management) , econometrics , statistics , engineering , control system , artificial intelligence , machine learning , simulation , database , chemistry , biology , genetics , layer (electronics) , evolutionary biology , botany , organic chemistry , electrical engineering , motion (physics)
Identification of linear models in view of robust control design requires the identification of a control‐relevant nominal model, and a quantification of model uncertainty. In this paper a procedure is presented to quantify the model uncertainty of any prespecified nominal model, from a sequence of measurement data of input and output signals from a plant. By employing a nonparametric empirical transfer function estimate (ETFE), we are able to split the model uncertainty into three parts: the inherent uncertainty in the data due to data imperfections, the unmodelled dynamics in the nominal model, and the uncertainty due to interpolation. A frequency‐dependent hard error bound is constructed, and results are given for tightening the bound through appropriate input design.