We present a new iterative model order reduction method for large-scale linear time-invariant dynamical systems, based on a combined singular value decomposition–adaptive-order rational Arnoldi (SVD-AORA) approach. This method is an extension of the SVD-rational Krylov method. It is based on two-sided projections: the SVD side depends on the observability Gramian by the resolution of the Lyapunov equation, and the Krylov side is generated by the adaptive-order rational Arnoldi based on moment matching. The use of the SVD provides stability for the reduced system, and the use of the AORA method provides numerical efficiency and a relative lower computation complexity. The reduced model obtained is asymptotically stable and minimizes the error (\(H_{2}\) and \(H_{\infty }\)) between the original and the reduced system. Two examples are given to study the performance of the proposed approach. doi:10.1017/S1446181117000049
@article{9622,
title = {An iterative model order reduction method for large-scale dynamical systems},
journal = {ANZIAM Journal},
volume = {58},
year = {2017},
doi = {10.21914/anziamj.v59i0.9622},
language = {EN},
url = {http://dml.mathdoc.fr/item/9622}
}
Mohamed, Kouki; Mehdi, Abbes; Abdelkader, Mami. An iterative model order reduction method for large-scale dynamical systems. ANZIAM Journal, Tome 58 (2017) . doi : 10.21914/anziamj.v59i0.9622. http://gdmltest.u-ga.fr/item/9622/