Improved Closed-Loop Subspace Identification with Prior Information Using Principal Component Analysis and Column Weighting

It has been proven that integrating prior information into subspace identification methods (SIMs) can improve the accuracy of model identification. In this study, a new closed-loop SIM integrating prior information is proposed based on principal component analysis (PCA) and column weighting. Compared with the conventional SIMs based on PCA, the constrained least square (CLS) approach is used to incorporate prior information into the impulse response after the PCA procedure. Then the system matrices can be extracted from the estimated impulse response parameters. The simulation results demonstrate that the proposed method is more accurate and stable for model identification.

[1]  K. Leuven,et al.  SUBSPACE IDENTIFICATION METHODS , 2007 .

[2]  S. Ding,et al.  Closed-loop subspace identification: an orthogonal projection approach , 2004 .

[3]  Mohamed Soliman,et al.  Subspace identification with prior steady-state information , 2010, The 2010 International Conference on Computer Engineering & Systems.

[4]  Tohru Katayama,et al.  An approach to closed-loop subspace identification by orthogonal decomposition , 2007, Autom..

[5]  Biao Huang,et al.  Bias-eliminated subspace model identification under time-varying deterministic type load disturbance , 2015 .

[6]  Dong Shen,et al.  Fault diagnosis and compensation for two‐dimensional discrete time systems with sensor faults and time‐varying delays , 2017 .

[7]  Donghua Zhou,et al.  Control Performance Assessment for ILC-Controlled Batch Processes in a 2-D System Framework , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[8]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[9]  J. W. van Wingerden,et al.  A kernel based approach for LPV subspace identification , 2015 .

[10]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[11]  Yong Wang,et al.  Subspace-based continuous-time identification of fractional order systems from non-uniformly sampled data , 2016, Int. J. Syst. Sci..

[12]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[13]  Changyin Sun,et al.  Linear parameter varying switching attitude control for a near space hypersonic vehicle with parametric uncertainties , 2015, Int. J. Syst. Sci..

[14]  Ibrahim Ziedan,et al.  Brief paper - Improved subspace identification with prior information using constrained least squares , 2011 .

[15]  Guillaume Mercère,et al.  Image modeling based on a 2-D stochastic subspace system identification algorithm , 2016, Multidimensional Systems and Signal Processing.

[16]  Tor Arne Johansen,et al.  Constrained and regularized system identification , 1997 .

[17]  Steven X. Ding,et al.  Unbiased Minimum Variance Fault and State Estimation for Linear Discrete Time-Varying Two-Dimensional Systems , 2017, IEEE Transactions on Automatic Control.

[18]  Lennart Ljung,et al.  Handling Certain Structure Information in Subspace Identification , 2009 .

[19]  Torben Knudsen.  Consistency analysis of subspace identification methods based on a linear regression approach , 2001, Autom..

[20]  L. Ljung,et al.  On consistency of closed-loop subspace identification with innovation estimation , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[21]  Alessandro Chiuso,et al.  Consistency analysis of some closed-loop subspace identification methods , 2005, Autom..

[22]  Sun-Yuan Kung,et al.  A new identification and model reduction algorithm via singular value decomposition , 1978 .

[23]  W. Larimore.  System Identification, Reduced-Order Filtering and Modeling via Canonical Variate Analysis , 1983, 1983 American Control Conference.

[24]  Hajime Ase,et al.  A subspace-based identification of Wiener–Hammerstein benchmark model , 2015 .

[25]  Bao-chang Xu,et al.  New identification method for Hammerstein models based on approximate least absolute deviation , 2016, Int. J. Syst. Sci..

[26]  Si-Zhao Joe Qin,et al.  An overview of subspace identification , 2006, Comput. Chem. Eng..

[27]  Alessandro Chiuso,et al.  The role of vector autoregressive modeling in predictor-based subspace identification , 2007, Autom..

[28]  Pavel Trnka,et al.  Subspace like identification incorporating prior information , 2009, Autom..

[29]  U. Kruger,et al.  Dynamic Principal Component Analysis Using Subspace Model Identification , 2005, ICIC.

[30]  Ivan Markovsky,et al.  Subspace identification with constraints on the impulse response , 2017, Int. J. Control.

[31]  Jinxu Cheng,et al.  Closed-loop subspace identification with prior information , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[32]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[33]  Youqing Wang,et al.  Subspace identification for closed-loop 2-D separable-in-denominator systems , 2017, Multidimens. Syst. Signal Process..

[34]  Enrique del Castillo,et al.  Closed-Loop System Identification for Small Samples With Constraints , 2007, Technometrics.

[35]  Ahmed Mahmoud Abdelrahman Elanany.  Improved subspace identication with prior information using constrained least-squares , 2011 .

[36]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[37]  Feng Ding,et al.  Least squares based iterative algorithm for pseudo-linear autoregressive moving average systems using the data filtering technique , 2015, J. Frankl. Inst..

[38]  Lennart Ljung,et al.  Closed-Loop Subspace Identification with Innovation Estimation , 2003 .

[39]  Roy S. Smith,et al.  Frequency Domain Subspace Identification Using Nuclear Norm Minimization and Hankel Matrix Realizations , 2014, IEEE Transactions on Automatic Control.

[40]  Jin Wang,et al.  Closed-loop subspace identification using the parity space , 2006, Autom..