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 …

Subspace methods for identification of linear time-invariant dynamical systems typically
consist of two main steps. First, a so-called subspace estimate is constructed. This first step …

Asymptotic normality for a class of subspace algorithms, which estimate the state in a first
step, is derived. Expressions for the asymptotic variance are given. Linear systems with …

In the literature results can be found which claim consistency for the subspace method under
certain quite weak assumptions. Unfortunately, a new result gives a counter example …

A parameter estimation method for finite-dimensional multivariate linear stochastic systems,
which is guaranteed to produce valid models approximating the true underlying system in a …

As the title suggests, this thesis can be divided into several parts. On the one hand it
considers identi cation as well as control, on the other hand linear as well as bilinear …

Explicit formulae of dominant parts of the estimation errors of plant A matrix in PI-MOESP,
PO-MOESP, and one of N4SID methods are derived based on a proposing lemma on …

This paper is concerned with the perturbation error analysis of the widely-used Subspace
Identification Methods (SIM) for n-dimensional discrete-time Multiple-Input Multiple-Output …

The common theme of the thesis is the derivation and analysis of subspace parameter
estimation methods. In particular, this thesis is concerned with the identification of linear time …

This paper proposes a consistent estimates of the Kalman gain and the covariance of the
innovations in the framework of subspace identification. The estimates of the system …