Model reduction by least squares moment matching for linear and nonlinear systems

TL;DR

This paper introduces a novel approach for model reduction in linear and nonlinear systems using least squares moment matching, providing new theoretical insights and practical parameterizations.

Contribution

It develops a unified framework for least squares moment matching in both linear and nonlinear systems, with new characterizations and geometric interpretations.

Findings

New time-domain characterization of least squares moment matching

Parameterizations with geometric and system-theoretic interpretations

Numerical examples demonstrating effectiveness

Abstract

The paper addresses the model reduction problem for linear and nonlinear systems using the notion of least squares moment matching. For linear systems, the main idea is to approximate a transfer function by ensuring that the interpolation conditions imposed by moment matching are satisfied in a least squares sense. The paper revisits this idea using tools from output regulation theory to provide a new time-domain characterization of least squares moment matching. It is shown that least squares moment matching can be characterized in terms of an optimization problem involving an invariance equation and in terms of the steady-state behavior of an error system. This characterization, in turn, is then used to define a nonlinear enhancement of the notion of least squares moment matching and to develop a model reduction theory for nonlinear systems based on the notion of least squares moment matching. Parameterized families of models achieving least squares moment matching are determined both for linear and nonlinear systems. The new parameterizations are shown to admit natural geometric and system-theoretic interpretations. The theory is illustrated by worked-out numerical examples.