In this section
@inproceedings{Shakib:2025:10.23919/ECC65951.2025.11187025,
author = {Shakib, F and Scarciotti, G and Astolfi, A},
doi = {10.23919/ECC65951.2025.11187025},
pages = {3207--3212},
title = {Physics-based and data-driven modeling for linear systems using moment matching},
url = {http://dx.doi.org/10.23919/ECC65951.2025.11187025},
year = {2025}
}
TY - CPAPER
AB - First-principle models often fail to accurately capture system dynamics due to modeling simplifications and parameter uncertainties. This article introduces a data-driven technique for linear systems, enhancing baseline first-principle state-space models with black-box models obtained from experimental steady-state data. The proposed method parameterises models that achieve moment matching by integrating a known baseline model with a black-box component. Tools are provided to enforce a known interconnection structure or other physical knowledge. A mass-spring-damper system demonstrates the effectiveness of the technique.
AU - Shakib,F
AU - Scarciotti,G
AU - Astolfi,A
DO - 10.23919/ECC65951.2025.11187025
EP - 3212
PY - 2025///
SP - 3207
TI - Physics-based and data-driven modeling for linear systems using moment matching
UR - http://dx.doi.org/10.23919/ECC65951.2025.11187025
ER -