Review: Propagator-based methods for recursive subspace model identification

  • Authors:

  • Guillaume Mercère;Laurent Bako;Stéphane Lecuche

  • Affiliations:

  • Université de Poitiers, Laboratoire d'Automatique et d'Informatique Industrielle (EA 1219), 40 avenue du recteur Pineau, 86022 Poitiers, France;Département Informatique et Automatique, ícole des Mines de Douai, 941, rue Charles Bourseul B.P. 838, 59508 Douai Cedex, France and Laboratoire d'Automatique, Génie Informatique et ...;Département Informatique et Automatique, ícole des Mines de Douai, 941, rue Charles Bourseul B.P. 838, 59508 Douai Cedex, France and Laboratoire d'Automatique, Génie Informatique et ...

  • Venue:
  • Signal Processing
  • Year:
  • 2008

Quantified Score

Hi-index 0.08

Visualization

Abstract

The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO Output Error State-Space (MOESP) identification class, are developed. The main goals of the proposed methods are to circumvent the huge complexity of eigenvalues or singular values decomposition techniques used by the offline algorithm and to provide consistent state-space matrices estimates in a noisy framework. The underlying principle consists in using the relationship between array signal processing and subspace identification to adjust the propagator method (originally developed in array signal processing) to track the subspace spanned by the observability matrix. The problem of the (coloured) disturbances acting on the system is solved by introducing an instrumental variable in the minimized cost functions. A particular attention is paid to the algorithmic development and to the computational cost. The benefits of these algorithms in comparison with existing methods are emphasized with a simulation study in time-invariant and time-varying scenarios.