Model Reduction of Second-Order Systems

Abstract

In this paper we propose new model reduction methods which preserve the polynomial form of a given second order system. We also give numerical results to illustrate that even when imposing such restrictions, one still obtains approximation errors which are comparable to those obtained via balanced truncation. The advantage of our approach is that preserving the system structure may better reflect the physical properties of the system we want to approximate.