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Title: Two nonlinear optimization methods for black box identification compared
Authors: Van Mulders, Anne ×
Schoukens, Johan
Volckaert, Marnix
Diehl, Moritz #
Issue Date: Oct-2010
Publisher: Pergamon Press
Series Title: Automatica vol:46 issue:10 pages:1675-1681
Abstract: In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate the parameters of e.g. a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization of the same cost function. While the first method does not estimate the states explicitly, the second method estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation data. The unconstrained method appears to be faster and more memory efficient, but the constrained method has a significant advantage as well: it is robust for unstable systems of which bounded input-output data can be measured (e.g. a system captured in a stabilizing feedback loop). Both methods have successfully been applied on real-life measurement data.
ISSN: 0005-1098
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Production Engineering, Machine Design and Automation (PMA) Section
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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