Identification of Highly Accurate Low Order State Space Models in the Frequency Domain

Abstract

This paper discusses an integrated approach for high accuracy and low order model identification. The approach integrates subspace-based identification algorithms with model reduction and parameter estimation algorithms to generate highly accmate low order models. The specific identification algorithm presented in the paper is the Integrated Frequency domain Observability Range Space Extraction and Least Square parameter estimation algorithm (IFORSELS). IFORSELS is an iterative algorithm which integrates the Frequency domain Observability Range Space Extraction (FORSE) algorithm, the Balanced Realization model reduction algorithm, and the Logarithmic and Additive Least Square parameter estimation algorithms in an iterativp fashion. It is capable of achieving much higher modeling accmacy using a lower order model than that achieved using a higher order model by subspace-based algorithms, such as FORSE.