Balanced parametrization of classes of linear systems
SIAM Journal on Control and Optimization
On-line structure selection for multivariable state-space models
Automatica (Journal of IFAC)
Brief paper: Constrained state-space system identification with application to structural dynamics
Automatica (Journal of IFAC)
Identification of state-space models by modified nonlinear LS optimization method
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Brief paper: Two nonlinear optimization methods for black box identification compared
Automatica (Journal of IFAC)
Robust maximum-likelihood estimation of multivariable dynamic systems
Automatica (Journal of IFAC)
An analysis of the parametrization by data driven local coordinates for multivariable linear systems
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In this paper we introduce a new parametrization for state-space systems: data driven local coordinates (DDLC). The parametrization is obtained by restricting the full state-space parametrization, where all matrix entries are considered to be free, to an affine plane containing a given nominal state-space realization. This affine plane is chosen to be perpendicular to the tangent space to the manifold of observationally equivalent state-space systems at the nominal realization. The application of the parametrization to prediction error identification is exemplified. Simulations indicate that the proposed parametrization has numerical advantages as compared to e.g. the more commonly used observable canonical form.