Robust identification of process models from plant data

Title: Robust identification of process models from plant data
Creator: Goodwin, Graham C.; Aguero, Juan C.; Welsh, James S.; Yuz, Juan I.; Adams, Gregory J.; Rojas, Cristian R.
Relation: Journal of Process Control Vol. 18, Issue 9, p. 810-820
Publisher Link: http://dx.doi.org/10.1016/j.jprocont.2008.06.004
Publisher: Elsevier
Resource Type: journal article
Date: 2008
Description: A precursor to any advanced control solution is the step of obtaining an accurate model of the process. Suitable models can be obtained from phenomenological reasoning, analysis of plant data or a combination of both. Here, we will focus on the problem of estimating (or calibrating) models from plant data. A key goal is to achieve robust identification. By robust we mean that small errors in the hypotheses should lead to small errors in the estimated models. We argue that, in some circumstances, it is essential that special precautions, including discarding some part of the data, be taken to ensure that robustness is preserved. We present several practical case studies to illustrate the results.
Subject: closed loop identification; robust identification
Identifier: http://hdl.handle.net/1959.13/43066
Identifier: uon:5195
Identifier: ISSN:0959-1524
Language: eng
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