Algorithms for Subspace State-Space System Identification: An Overview

Abstract

We give a general overview of the state of the art in subspace system identification methods. We have restricted ourselves to the most important ideas and developments since the methods appeared in the late eighties. First the basics of linear subspace identification are summarized. Different algorithms one finds in literature (such as N4SID IV-4SID MOESP CVA) are discussed and put into a unifying framework. Further a comparison between subspace identification and prediction error methods is made on the basis of computational complexity and precision of the methods by applying them on 10 industrial datasets. Also the issue of statistical consistency of subspace identification methods is briefly discussed. Some extensions of linear subspace methods to other classes of systems such as continuous-time systems bilinear systems time-periodic systems etc. are given. Current software developments and trends in the field of system identification are also discussed including a general public domain database called DAISY containing numerous datasets that can be used to validate system identification algorithms.