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论文类型：会议论文

发表时间：2012-07-10

收录刊物：EI、Scopus

卷号：8

期号：PART 1

页面范围：166-171

摘要：For model identification of industrial operating systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem, a subspace identification method is proposed in this paper by developing an orthogonal projection approach to guarantee consistent estimation of the deterministic part of such a system. The rank condition for such orthogonal projection is analyzed in terms of the nominal state-space model structure. Using the principal component analysis (PCA), the extended observability matrix and low triangular block-Toeliptz matrix of the state-space model are analytically derived. Accordingly, the system state-space matrices can be retrieved in a transparent manner from the above matrices through linear algebra or an ordinary least-squares (LS) algorithm. A benchmark example used in the existing references is adopted to demonstrate the effectiveness and merit of the proposed subspace identification method. © 2012 IFAC.