An extended closed-loop subspace identification method for error-in-variables systems
A closed-loop subspace identification method is proposed for industrial operating systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Based on using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and low triangular block-Toeliptz matrix of the system state-space model. In the result, the system state-space matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.
Closed-loop error-in-variables system subspace identification extended observability matrix orthogonal projection.
Tao Liu Cheng Shao
Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024
国内会议
厦门
英文
1-6
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）