Relative Error-based Time-limited H2 Model Order Reduction via Oblique Projection

TL;DR

This paper introduces a new oblique projection algorithm for time-limited H2 model reduction based on relative error, which avoids large-scale Lyapunov and Riccati equations and outperforms existing methods within a specified time interval.

Contribution

A novel oblique projection method for time-limited H2 model reduction using relative error, eliminating the need for large-scale Lyapunov and Riccati equations, and demonstrating superior performance.

Findings

The proposed algorithm achieves smaller H2-norm relative error within the time interval.

Numerical results show the algorithm outperforms existing methods like balanced truncation.

The method is computationally efficient due to avoiding large-scale matrix equations.

Abstract

In time-limited model order reduction, a reduced-order approximation of the original high-order model is obtained that accurately approximates the original model within the desired limited time interval. Accuracy outside that time interval is not that important. The error incurred when a reduced-order model is used as a surrogate for the original model can be quantified in absolute or relative terms to access the performance of the model reduction algorithm. The relative error is generally more meaningful than an absolute error because if the original and reduced systems' responses are of small magnitude, the absolute error is small in magnitude as well. However, this does not necessarily mean that the reduced model is accurate. The relative error in such scenarios is useful and meaningful as it quantifies percentage error irrespective of the magnitude of the system's response. In this paper, the necessary conditions for a local optimum of the time-limited H2 norm of the relative error system are derived. Inspired by these conditions, an oblique projection algorithm is proposed that ensures small H2-norm relative error within the desired time interval. Unlike the existing relative error-based model reduction algorithms, the proposed algorithm does not require solutions of large-scale Lyapunov and Riccati equations. The proposed algorithm is compared with time-limited balanced truncation, time-limited balanced stochastic truncation, and time-limited iterative Rational Krylov algorithm. Numerical results confirm the superiority of the proposed algorithm over these existing algorithms.