Abstract
This paper deals with model order reduction of random parameter-dependent (RPD) linear time invariant (LTI) systems by using the RPD-balanced realization (BR), recently developed by the same authors. A novel way to deal with this crucial issue is presented. It is based on the intrusive Galerkin projection of the RPD-BR. Indeed, it is shown, through numerical simulations, that the intrusive Galerkin projection of the RPD-BR into the generalized polynomial chaos (GPC) space enables to generate a deterministic realization preserving stability properties for the original RPD-model. Moreover, the new deterministic state variables, which are the stochastic modes of the RPD-balanced state variables, are ordered with respect to their sub-contributions to the RPD-input/output behaviour, measured by deterministic Hankel values. Hence, a deterministic reduced order model is derived by deleting the state variables with small Hankel values and the complete RPD-reduced order model is then obtained. The feasibility of the proposed method is analyzed while its accuracy is compared to the RPD-truncated balanced realization (RPD-TBR).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
