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论文类型：期刊论文

发表时间：2015-01-01

发表刊物：JOURNAL OF PROCESS CONTROL

收录刊物：SCIE、EI

卷号：25

页面范围：41-49

ISSN号：0959-1524

关键字：Subspace identification; Orthogonal projection; Singular value decomposition; Extended observability matrix; Rank condition

摘要：Unexpected or time-varying deterministic type load disturbances are often encountered when performing identification tests in practical applications. A bias-eliminated subspace identification method is proposed in this paper by developing an orthogonal projection approach to guarantee consistent estimation on the deterministic part of the plant, in combination with a Maclaurin time series approximation on the output response arising from deterministic type load disturbance. The rank condition for such an orthogonal projection is disclosed in terms of the state-space model structure adopted for identification. Using principal component analysis (PCA), the extended observability matrix and the lower triangular Toeplitz matrix of the state-space model are explicitly derived. Accordingly, the plant state-space matrices can be retrieved from the above matrices through a shift-invariant algorithm. A benchmark example from the literature and an illustrative example of industrial injection molding are used to demonstrate the effectiveness and merit of the proposed identification method. (C) 2014 Elsevier Ltd. All rights reserved.