Quantification of model uncertainty from data
- 1 January 1994
- journal article
- research article
- Published by Wiley in International Journal of Robust and Nonlinear Control
- Vol. 4 (2) , 301-319
- https://doi.org/10.1002/rnc.4590040206
Abstract
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.Keywords
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