Large-Scale Estimation of Dominant Poles of a Transfer Function by an Interpolatory Framework

TL;DR

This paper presents a scalable interpolatory subspace framework for estimating dominant poles of large-scale descriptor systems, improving reliability over existing methods and providing convergence guarantees.

Contribution

It introduces a novel iterative subspace method that efficiently estimates dominant poles with proven quadratic convergence for large-scale systems.

Findings

Framework outperforms SAMDP in numerical tests

Proven at-least-quadratic convergence

Effective for large-scale descriptor systems

Abstract

We focus on the dominant poles of the transfer function of a descriptor system. The transfer function typically exhibits large norm at and near the imaginary parts of the dominant poles. Consequently, the dominant poles provide information about the points on the imaginary axis where the ${\mathcal L}_\infty$ norm of the system is attained, and they are also sometimes useful to obtain crude reduced-order models. For a large-scale descriptor system, we introduce a subspace framework to estimate a prescribed number of dominant poles. At every iteration, the large-scale system is projected into a small system, whose dominant poles can be computed at ease. Then the projection spaces are expanded so that the projected system after subspace expansion interpolates the large-scale system at the computed dominant poles. We prove an at-least-quadratic-convergence result for the framework, and provide numerical results confirming this. On real benchmark examples, the proposed framework appears to be more reliable than SAMDP [IEEE Trans. Power Syst. 21, 1471-1483, 2006], one of the widely used algorithms due to Rommes and Martins for the estimation of the dominant poles.