Time-limited H2-optimal Model Order Reduction of Linear Systems with Quadratic Outputs

TL;DR

This paper develops a time-limited H2-optimal model order reduction method for linear systems with quadratic outputs, deriving error measures, optimality conditions, and an iterative algorithm, demonstrated through numerical examples.

Contribution

It introduces a novel time-limited H2-norm for quadratic output systems, along with optimality conditions and an iterative algorithm for model reduction within a specified time interval.

Findings

The derived time-limited H2-norm effectively measures model accuracy within the time window.

The proposed algorithm converges to a stationary point satisfying most optimality conditions.

Numerical examples confirm the method's ability to accurately approximate high-order models.

Abstract

An important class of dynamical systems with several practical applications is linear systems with quadratic outputs. These models have the same state equation as standard linear time-invariant systems but differ in their output equations, which are nonlinear quadratic functions of the system states. When dealing with models of exceptionally high order, the computational demands for simulation and analysis can become overwhelming. In such cases, model order reduction proves to be a useful technique, as it allows for constructing a reduced-order model that accurately represents the essential characteristics of the original high-order system while significantly simplifying its complexity. In time-limited model order reduction, the main goal is to maintain the output response of the original system within a specific time range in the reduced-order model. To assess the error within this time interval, a mathematical expression for the time-limited $\mathcal{H}_2$-norm is derived in this paper. This norm acts as a measure of the accuracy of the reduced-order model within the specified time range. Subsequently, the necessary conditions for achieving a local optimum of the time-limited $\mathcal{H}_2$ norm error are derived. The inherent inability to satisfy these optimality conditions within the Petrov-Galerkin projection framework is also discussed. After that, a stationary point iteration algorithm based on the optimality conditions and Petrov-Galerkin projection is proposed. Upon convergence, this algorithm fulfills three of the four optimality conditions. To demonstrate the effectiveness of the proposed algorithm, a numerical example is provided that showcases its ability to effectively approximate the original high-order model within the desired time interval.