Consistency analysis of subspace identification methods based on a linear regression approach

DOI10.1016/S0005-1098(00)00125-4zbMath0967.93028OpenAlexW2014380849MaRDI QIDQ1592896

Torben Knudsen

Publication date: 30 August 2001

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0005-1098(00)00125-4

zbMATH Keywords

identificationconsistencylinear regressionleast squares approachsubspace estimationlinear state-space model

Mathematics Subject Classification ID

System identification (93B30) Least squares and related methods for stochastic control systems (93E24)

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Robust data driven \(H_\infty\) control for wind turbine ⋮ Predictor-Based Tensor Regression (PBTR) for LPV subspace identification ⋮ Subspace identification for non-linear systems with measured-input non-linearities ⋮ Asymptotic properties of subspace estimators ⋮ Closed‐loop identification of the time‐varying dynamics of variable‐speed wind turbines ⋮ Subspace identification of bilinear and LPV systems for open- and closed-loop data ⋮ Tensor networks for MIMO LPV system identification ⋮ Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics ⋮ A novel subspace identification approach with enforced causal models ⋮ Improved closed-loop subspace identification with prior information

Cites Work

• A linear regression approach to state-space subspace system identification
• Statistical analysis of novel subspace identification methods
• Identifying MIMO Wiener systems using subspace model identification methods
• On consistency of subspace methods for system identification
• Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
• Subspace-based methods for the identification of linear time-invariant systems
• Consistency and relative efficiency of subspace methods
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