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Data-driven model reduction by two-sided moment matching
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DOI：10.1016/j.automatica.2024.111702.png)
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Abstract
En 中文
In this brief paper, we propose a time-domain data-driven method for model order reduction by two-sided moment matching for linear systems. An algorithm that asymptotically approximates a key interpolation matrix from time-domain samples of the so-called two-sided interconnection is provided. Exploiting this estimated interpolation matrix, we determine the unique reduced-order model of order v, which asymptotically matches the moments at 2v distinct interpolation points. Furthermore, we discuss the impact that certain disturbances and data distortions may have on the algorithm. Finally, we illustrate the use of the proposed methodology by means of a benchmark model. (c) 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:
Model reduction
Data-driven
Moment matching
System identification
Time-domain
Two-sided
Linear systems
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