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Paper details
Number 4 - December 2016
Volume 26 - 2016
Identification of parametric models with a priori knowledge of process properties
Krzysztof B. Janiszowski, Paweł Wnuk
Abstract
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.
Keywords
absolute error measure, constrained parameters estimation, identification, parametric MISO models