Orthogonal projection based subspace identification against colored noise View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-02

AUTHORS

Jie Hou, Tao Liu, Fengwei Chen

ABSTRACT

In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eliminate the influence of colored noise for consistent estimation of the extended observability matrix of the plant state-space model. A shift-invariant approach is then given to retrieve the system matrices from the estimated extended observability matrix. The persistent excitation condition for consistent estimation of the extended observability matrix is analyzed. Moreover, a numerical algorithm is given to compute the estimation error of the estimated extended observability matrix. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method. More... »

PAGES

69-77

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11768-017-6003-7

DOI

http://dx.doi.org/10.1007/s11768-017-6003-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016413528


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