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Date of Publication:2016-01-01

Journal:自动化学报

Issue:11

Page Number:1657-1663

ISSN No.:0254-4156

Abstract:In this paper, a closed-loop subspace model identification method using innovation estimation and orthogonal projection is proposed for closed-loop control systems. Firstly, a least-squares algorithm is adopted to estimate the innovation matrix via the vector autoregressive with exogenous inputs (VARX) model. Then, by performing an orthogonal pro jection of the observed input-output Hankel matrix onto the orthogonal complement space of innovation Hankel matrix to eliminate the influence from noise, the extended observability matrix and lower triangular block Toeplitz matrix are derived from the parity space of noise-free input-output data. Finally, the system matrices are retrieved by using a shift-invariant approach. The consistent estimation conditions are analyzed with a strict proof. A simulation example is shown to demonstrate the effectiveness and merit of the proposed method.

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