Frequency-weighted model order reduction combined with the model decomposition
Wybrane pełne teksty z tego czasopisma
Physical objects, in practice, are frequently represented by the LTI (Linear Time Invariant) models consisting of linear differential equations with constant coefficients. The LTI models of complex objects often have a large size. Designing a control system or performing a realtime object simulation on large-scale models may not be possible. Model Order Reduction (MOR) is an operation performed to reduce the size of a model with retaining its most important features. In the case where the reduced model has to approximate the frequency characteristics of the original model within the given range of adequacy, the Frequency-Weighted (FW) method should be used. The FW method requires to select the appropriate parameters of the input and / or output filters in order to obtain the best results. This selection is not an unique operation - which means that it is advisable to use an evolutionary algorithm that performs multiple reduction operations to match the relevant parameters. It is possible to reduce the computational complexity of the FW reduction process by decomposing the model into parts. The purpose of the paper is to compare the Frequency-Weighted order reduction combined with a various model decompositions based on the Schur complement, Schur and the Schur-Sylvester methods. The research is conducted on linearized models of the one-phase zone of the evaporator of the once-through steam BP-1150 boiler. The stability and accuracy of the reduced models are analyzed.
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Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).