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Missing point estimation in models described by proper orthogonal decomposition

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4 Author(s)
Astrid, P. ; Dept. of Electr. Eng., Eindhoven Univ. of Technol., Netherlands ; Weiland, S. ; Willcox, K. ; Backx, T.

The method of proper orthogonal decomposition (POD) has been proven to be very useful for constructing low dimensional models of large scale systems. However, despite the model order reduction, low-order models derived from truncations of POD bases remain computationally intensive for the simulation of large scale linear time-varying (LTV) and nonlinear models. The main bottleneck lies in the requirement to have full spatial information from the original model to construct the reduced-order models. In this paper, we propose criteria to select a suitable subset of the original spatial coordinate system using information from the snapshot matrix and the POD basis functions. We show that the states of the POD-based reduced order model can be estimated much more efficiently by conducting projections on these selected states. The method is applied to a representative industrial model of a glass feeder.

Published in:

Decision and Control, 2004. CDC. 43rd IEEE Conference on  (Volume:2 )

Date of Conference:

14-17 Dec. 2004