Numerical conditioning and asymptotic variance of subspace estimates

Authors:
Alessandro Chiuso;Giorgio Picci
Affiliations:
Department of Information Engineering, University of Padova, Padova 35131, Italy;Department of Information Engineering, University of Padova, Padova 35131, Italy
Venue:
Automatica (Journal of IFAC)
Year:
2004

Citing 5
Cited 5

N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems

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Some facts about the choice of the weighting matrices in Larimore type of subspace algorithms

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Analysis of the asymptotic properties of the MOESP type of subspace algorithms

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Subspace identification by data orthogonalization and model decoupling

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On the ill-conditioning of subspace identification with inputs

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Asymptotic variance of subspace methods by data orthogonalization and model decoupling: a comparative analysis

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Subspace identification by data orthogonalization and model decoupling

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Asymptotic properties of subspace estimators

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Consistency analysis of some closed-loop subspace identification methods

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On the ill-conditioning of subspace identification with inputs

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Abstract

New formulas for the asymptotic variance of the parameter estimates in subspace identification, show that the accuracy of the parameter estimates depends on certain indices of 'near collinearity' of the state and future input subspaces of the system to be identified. This complements the numerical conditioning analysis of subspace methods presented in the companion paper (On the ill-conditioning of subspace identification with inputs, Automatica, doi:10.1016/j.automatica.2003.11.009).