System identification: theory for the user
System identification: theory for the user
The statistical theory of linear systems
The statistical theory of linear systems
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Subspace algorithms for the stochastic identification problem
Automatica (Journal of IFAC)
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Automatica (Journal of IFAC)
Statistical analysis of novel subspace identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
Subspace identification from closed loop data
Signal Processing - Special issue: subspace methods, part II: system identification
Some facts about the choice of the weighting matrices in Larimore type of subspace algorithms
Automatica (Journal of IFAC)
Order estimation for subspace methods
Automatica (Journal of IFAC)
Analysis of the asymptotic properties of the MOESP type of subspace algorithms
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief Subspace identification of closed loop systems by the orthogonal decomposition method
Automatica (Journal of IFAC)
Asymptotic properties of subspace estimators
Automatica (Journal of IFAC)
Consistency analysis of some closed-loop subspace identification methods
Automatica (Journal of IFAC)
On the ill-conditioning of subspace identification with inputs
Automatica (Journal of IFAC)
Subspace identification of Bilinear and LPV systems for open- and closed-loop data
Automatica (Journal of IFAC)
Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems
Computers & Mathematics with Applications
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Subspace identification for closed loop systems has been recently studied by several authors. A class of new and consistent closed-loop subspace algorithms is based on identification of a predictor model, in a way similar as prediction error methods (PEM) do. Experimental evidence suggests that these methods have a behavior which is very close to PEM in certain examples. The asymptotical statistical properties of one of these methods have been studied recently allowing to show (i) its relation with CCA and (ii) that Cramer-Rao lower bound is not reached in general. Very little, however, is known concerning their relative performance. In this paper we shall discuss the link between these ''predictor-based'' methods; to this purpose we exploit the role which Vector Auto Regressive with eXogenous input models play in all these algorithms. The results of this paper provide a unifying framework under which all these algorithms can be viewed; also the link with VARX modeling have important implications as to computational complexity is concerned, leading to very computationally attractive implementations. We also hope that this framework, and in particular the relation with VARX modeling followed by model reduction will turn out to be useful in future developments of subspace identification, such as the quest for efficient procedures and the statistical analysis with finite-data.