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  • 标题:Subspace Hammerstein Model Identification under Periodic Disturbance
  • 本地全文:下载
  • 作者:Jie Hou ; Tao Liu ; Bo Wahlberg
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:15
  • 页码:335-340
  • DOI:10.1016/j.ifacol.2018.09.157
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn this paper, a subspace identification method is proposed for Hammerstein systems under periodic disturbance. By using the linear superposition principle to decompose the periodic disturbance response from the deterministic system response, an orthogonal projection is established to eliminate the disturbance effect. The unknown disturbance period can be estimated by defining an objective function of output prediction error for minimization. Correspondingly, a singular value decomposition (SVD) based algorithm is given to estimate the observability matrix and the lower triangular block-Toeplitz matrix. The state matricesAandCare subsequently retrieved from the estimated observability matrix via a shift-invariant algorithm, while the input matrixBand the nonlinear input function parameters are retrieved from the estimated lower triangular block-Toeplitz matrix by an SVD approach. Consistent estimation of the observability matrix and the lower triangular block-Toeplitz matrix is analyzed. An illustrative example is shown to demonstrate the effectiveness of the proposed identification method.
  • 关键词:KeywordsSubspace identificationHammerstein systemPeriodic disturbanceConsistent estimation
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