Identification of parametric models with a priori knowledge of process properties

Krzysztof Janiszowski , Paweł Wnuk


An approach to estimation of a parametric discrete-time model of a process in the case of some a priori knowledge of the investigated process properties is presented. The knowledge of plant properties is introduced in the form of linear bounds, which can be determined for the coefficient vector of the parametric model studied. The approach yields special biased estimation of model coefficients that preserves demanded properties. A formula for estimation of the model coefficients is derived and combined with a recursive scheme determined for minimization of the sum of absolute model errors. The estimation problem of a model with known static gains of inputs is discussed and proper formulas are derived. This approach can overcome the non-identifiability problem which has been observed during estimation based on measurements recorded in industrial closed-loop control systems. The application of the proposed approach to estimation of a model for an industrial plant (a water injector into the steam flow in a power plant) is presented and discussed.
Author Krzysztof Janiszowski (FM / IACR)
Krzysztof Janiszowski,,
- The Institute of Automatic Control and Robotics
, Paweł Wnuk (FM / IACR)
Paweł Wnuk,,
- The Institute of Automatic Control and Robotics
Journal seriesInternational Journal of Applied Mathematics & Computer Science, ISSN 1641-876X
Issue year2016
Publication size in sheets0.5
Keywords in English absolute error measure, constrained parameters estimation, identification, parametric MISO models
ASJC Classification2604 Applied Mathematics; 2201 Engineering (miscellaneous); 1701 Computer Science (miscellaneous)
Languageen angielski
Score (nominal)25
Score sourcejournalList
ScoreMinisterial score = 25.0, 22-11-2019, ArticleFromJournal
Ministerial score (2013-2016) = 25.0, 22-11-2019, ArticleFromJournal
Publication indicators WoS Citations = 1; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.377; WoS Impact Factor: 2016 = 1.42 (2) - 2016=1.597 (5)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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