A New Numerical Approach to Transfer Functions of LTI-PDEs

In the present paper, we introduce a new approach for the numerical calculation of transfer functions of linear time-invariant partial differential equations in a non-parametric form, e.g. for a subsequent approximation using (non-) rational transfer functions. With the approach, the closed-form solution of the transfer function is represented using a state transition matrix. Thus, the value of the transfer function at explicit values of the complex frequency can be determined by numerically solving the matrix differential equation of the former. The idea is illustrated using an example. In addition, a comparison with the system’s closed-form solution is carried out and the advantages of the new approach over an established one based on a finite-dimensional approximation are shown.

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Publication Details

Journal
IFAC-PapersOnLine
Published
2025-01-01
DOI
https://doi.org/10.1016/j.ifacol.2025.03.018
Citations
1
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.32
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article

A New Numerical Approach to Transfer Functions of LTI-PDEs

Veit Hagenmeyer, Jan M. Schaßberger, Lutz Gröll
1 citations
IFAC-PapersOnLine
Model Reduction and Neural Networks
1.32
article

A New Numerical Approach to Transfer Functions of LTI-PDEs

Veit Hagenmeyer, Jan M. Schaßberger, Lutz Gröll
article en
1 citations

Abstract

In the present paper, we introduce a new approach for the numerical calculation of transfer functions of linear time-invariant partial differential equations in a non-parametric form, e.g. for a subsequent approximation using (non-) rational transfer functions. With the approach, the closed-form solution of the transfer function is represented using a state transition matrix. Thus, the value of the transfer function at explicit values of the complex frequency can be determined by numerically solving the matrix differential equation of the former. The idea is illustrated using an example. In addition, a comparison with the system’s closed-form solution is carried out and the advantages of the new approach over an established one based on a finite-dimensional approximation are shown.

IFAC-PapersOnLineVol. 59(1)
Karlsruhe Institute of Technology (DE)
Openalex Percentile: Top 21%
Model Reduction and Neural Networks
1.32
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