Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
A unifying theorem for three subspace system identification algorithms
Automatica (Journal of IFAC) - Special issue on trends in system identification
A linear regression approach to state-space subspace system identification
Signal Processing - Special issue: subspace methods, part II: system identification
Statistical optimality and canonical variate analysis system identification
Signal Processing - Special issue: subspace methods, part II: system identification
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
Subspace algorithms for the identification of multivarible dynamic errors-in-variables models
Automatica (Journal of IFAC)
Statistical analysis of subspace-based estimation of reduced-ranklinear regressions
IEEE Transactions on Signal Processing
On Consistency of Subspace Methods for System Identification
Automatica (Journal of IFAC)
Some facts about the choice of the weighting matrices in Larimore type of subspace algorithms
Automatica (Journal of IFAC)
Subspace-based system identification: weighting and pre-filtering of instruments
Automatica (Journal of IFAC)
Analysis of the asymptotic properties of the MOESP type of subspace algorithms
Automatica (Journal of IFAC)
Brief Consistency analysis of subspace identification methods based on a linear regression approach
Automatica (Journal of IFAC)
Asymptotic properties of subspace estimators
Automatica (Journal of IFAC)
Brief paper: An approach to closed-loop subspace identification by orthogonal decomposition
Automatica (Journal of IFAC)
Closed-loop subspace identification using the parity space
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Subspace identification methods (SIMs) for estimating state-space models have been proven to be very useful and numerically efficient. They exist in several variants, but all have one feature in common: as a first step, a collection of high-order ARX models are estimated from vectorized input-output data. In order not to obtain biased estimates, this step must include future outputs. However, all but one of the submodels include non-causal input terms. The coefficients of them will be correctly estimated to zero as more data become available. They still include extra model parameters which give unnecessarily high variance, and also cause bias for closed-loop data. In this paper, a new model formulation is suggested that circumvents the problem. Within the framework, the system matrices (A,B,C,D) and Markov parameters can be estimated separately. It is demonstrated through analysis that the new methods generally give smaller variance in the estimate of the observability matrix and it is supported by simulation studies that this gives lower variance also of the system invariants such as the poles.