| 摘要: |
| 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. |
| 关键词: Subspace identification, colored noise, orthogonal projection, extended observability matrix, consistent estimation |
| DOI： |
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| 基金项目:This work was supported by the National Thousand Talents Program of China, the National Natural Science Foundation of China (Nos. 61473054, 61633006), and the Fundamental Research Funds for the Central Universities of China (No. DUT15ZD108). |
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| Orthogonal projection based subspace identification against colored noise |
| J. Hou,T. Liu,F. Chen |
| (School of Control Science and Engineering, Dalian University of Technology, Dalian Liaoning 116024, China) |
| 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. |
| Key words: Subspace identification, colored noise, orthogonal projection, extended observability matrix, consistent estimation |
