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Journal articles on the topic 'Subspace identification methods'

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Author: Grafiati

Published: 7 July 2024

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1

Dalen, Christer, and David Di Ruscio. "On subspace system identification methods." Modeling, Identification and Control: A Norwegian Research Bulletin 43, no. 4 (2022): 119–30. http://dx.doi.org/10.4173/mic.2022.4.1.

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2

Viberg, Mats. "Subspace Methods in System Identification." IFAC Proceedings Volumes 27, no. 8 (July 1994): 1–12. http://dx.doi.org/10.1016/s1474-6670(17)47689-0.

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3

Joe Qin, S. "Subspace methods for system identification." Automatica 43, no. 4 (April 2007): 748–49. http://dx.doi.org/10.1016/j.automatica.2006.07.027.

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4

Avcıoğlu, Sevil, Ali Türker Kutay, and Kemal Leblebicioğlu. "Identification of Physical Helicopter Models Using Subspace Identification." Journal of the American Helicopter Society 65, no. 2 (April 1, 2020): 1–14. http://dx.doi.org/10.4050/jahs.65.022001.

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Subspace identification is a powerful tool due to its well-understood techniques based on linear algebra (orthogonal projections and intersections of subspaces) and numerical methods like singular value decomposition. However, the state space model matrices, which are obtained from conventional subspace identification algorithms, are not necessarily associated with the physical states. This can be an important deficiency when physical parameter estimation is essential. This holds for the area of helicopter flight dynamics, where physical parameter estimation is mainly conducted for mathematical model improvement, aerodynamic parameter validation, and flight controller tuning. The main objective of this study is to obtain helicopter physical parameters from subspace identification results. To achieve this objective, the subspace identification algorithm is implemented for a multirole combat helicopter using both FLIGHTLAB simulation and real flight-test data. After obtaining state space matrices via subspace identification, constrained nonlinear optimization methodologies are utilized for extracting the physical parameters. The state space matrices are transformed into equivalent physical forms via the "sequential quadratic programming" nonlinear optimization algorithm. The required objective function is generated by summing the square of similarity transformation equations. The constraints are selected with physical insight. Many runs are conducted for randomly selected initial conditions. It can be concluded that all of the significant parameters can be obtained with a high level of accuracy for the data obtained from the linear model. This strongly supports the idea behind this study. Results for the data obtained from the nonlinear model are also evaluated to be satisfactory in the light of statistical error analysis. Results for the real flight-test data are also evaluated to be good for the helicopter modes that are properly excited in the flight tests.

5

Miller, Daniel N., and Raymond A. de Callafon. "Subspace Identification From Classical Realization Methods." IFAC Proceedings Volumes 42, no. 10 (2009): 102–7. http://dx.doi.org/10.3182/20090706-3-fr-2004.00016.

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6

Mathieu, Pouliquen, and M'Saad Mohammed. "AN INTERPRETATION OF SUBSPACE IDENTIFICATION METHODS." IFAC Proceedings Volumes 38, no. 1 (2005): 904–9. http://dx.doi.org/10.3182/20050703-6-cz-1902.00152.

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7

Wani Jamaludin, Irma Wani Jamaludin, and Norhaliza Abdul Wahab. "Recursive Subspace Identification Algorithm using the Propagator Based Method." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 1 (April 1, 2017): 172. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp172-179.

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<p>Subspace model identification (SMI) method is the effective method in identifying dynamic state space linear multivariable systems and it can be obtained directly from the input and output data. Basically, subspace identifications are based on algorithms from numerical algebras which are the QR decomposition and Singular Value Decomposition (SVD). In industrial applications, it is essential to have online recursive subspace algorithms for model identification where the parameters can vary in time. However, because of the SVD computational complexity that involved in the algorithm, the classical SMI algorithms are not suitable for online application. Hence, it is essential to discover the alternative algorithms in order to apply the concept of subspace identification recursively. In this paper, the recursive subspace identification algorithm based on the propagator method which avoids the SVD computation is proposed. The output from Numerical Subspace State Space System Identification (N4SID) and Multivariable Output Error State Space (MOESP) methods are also included in this paper.</p>

8

Mohd-Mokhtar, Rosmiwati, and Liuping Wang. "Continuous time system identification using subspace methods." ANZIAM Journal 48 (June 26, 2007): 712. http://dx.doi.org/10.21914/anziamj.v47i0.1072.

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9

Muradore, Riccardo, and Enrico Fedrigo. "SUBSPACE IDENTIFICATION METHODS APPLIED TO ADAPTIVE OPTICS." IFAC Proceedings Volumes 39, no. 1 (2006): 943–48. http://dx.doi.org/10.3182/20060329-3-au-2901.00150.

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10

van der Veen, Gijs, Jan-Willem van Wingerden, Marco Lovera, Marco Bergamasco, and Michel Verhaegen. "Closed-loop subspace identification methods: an overview." IET Control Theory & Applications 7, no. 10 (July 4, 2013): 1339–58. http://dx.doi.org/10.1049/iet-cta.2012.0653.

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11

Jansson, Magnus. "Asymptotic Variance Analysis of Subspace Identification Methods." IFAC Proceedings Volumes 33, no. 15 (June 2000): 91–96. http://dx.doi.org/10.1016/s1474-6670(17)39732-x.

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12

Jansson, Magnus, and Bo Wahlberg. "On Consistency of Subspace System Identification Methods." IFAC Proceedings Volumes 29, no. 1 (June 1996): 4110–15. http://dx.doi.org/10.1016/s1474-6670(17)58324-x.

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13

Picci, Giorgio. "Statistical Properties of Certain Subspace Identification Methods." IFAC Proceedings Volumes 30, no. 11 (July 1997): 1043–49. http://dx.doi.org/10.1016/s1474-6670(17)42978-8.

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14

Nováková, J., M. Hromčík, and R. Jech. "Dynamic Causal Modeling and subspace identification methods." Biomedical Signal Processing and Control 7, no. 4 (July 2012): 365–70. http://dx.doi.org/10.1016/j.bspc.2011.07.002.

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15

Peternell, K., W. Scherrer, and M. Deistler. "Statistical analysis of novel subspace identification methods." Signal Processing 52, no. 2 (July 1996): 161–77. http://dx.doi.org/10.1016/0165-1684(96)00051-5.

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16

Di Ruscio, David. "A Bootstrap Subspace Identification Method: Comparing Methods for Closed Loop Subspace Identification by Monte Carlo Simulations." Modeling, Identification and Control: A Norwegian Research Bulletin 30, no. 4 (2009): 203–22. http://dx.doi.org/10.4173/mic.2009.4.2.

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17

Ni, Zhiyu, Jinguo Liu, and Zhigang Wu. "Identification of the time-varying modal parameters of a spacecraft with flexible appendages using a recursive predictor-based subspace identification algorithm." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 6 (April 26, 2018): 2032–50. http://dx.doi.org/10.1177/0954410018770560.

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This study focuses on the recursive identification of the time-varying modal parameters of on-orbit spacecraft caused by structural configuration changes. For this purpose, an algorithm called recursive predictor-based subspace identification is applied as an alternative method to improve the computational efficiency and noise robustness, and to implement an online identification of system parameters. In the existing time-domain identification methods, the eigensystem realization algorithm and subspace identification methods are usually applied to obtain the on-orbit spacecraft modal parameters. However, these approaches are designed based on a time-invariant system and singular value decomposition, which require a significant amount of computational time. Thus, these methods are difficult to employ for online identification. According to the adaptive filter theory, the recursive predictor-based subspace identification algorithm can not only avoid the singular value decomposition computation but also provide unbiased estimates in a general noisy framework using the recursive least squares approach. Furthermore, in comparison with the classical projection approximation subspace tracking series recursive algorithm, the recursive predictor-based subspace identification method is more suitable for systems with strong noise disturbances. By establishing the dynamics model of a large rigid-flexible coupling spacecraft, three cases of on-orbit modal parameter variation with time are investigated, and the corresponding system frequencies are identified using the recursive predictor-based subspace identification, projection approximation subspace tracking, and singular value decomposition methods. The results demonstrate that the recursive predictor-based subspace identification algorithm can be used to effectively perform an online parameter identification, and the corresponding computational efficiency and noise robustness are better than those of the singular value decomposition and projection approximation subspace tracking series approaches, respectively. Finally, the applicability of this method is also verified through a numerical simulation.

18

Chiuso, A. "Asymptotic Variance of Closed-Loop Subspace Identification Methods." IEEE Transactions on Automatic Control 51, no. 8 (August 2006): 1299–314. http://dx.doi.org/10.1109/tac.2006.878703.

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19

JANSSON, MAGNUS, and BO WAHLBERG. "On Consistency of Subspace Methods for System Identification." Automatica 34, no. 12 (December 1998): 1507–19. http://dx.doi.org/10.1016/s0005-1098(98)80004-6.

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20

Mercère, Guillaume, Laurent Bako, and Stéphane Lecœuche. "Propagator-based methods for recursive subspace model identification." Signal Processing 88, no. 3 (March 2008): 468–91. http://dx.doi.org/10.1016/j.sigpro.2007.09.012.

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21

Gustafsson, Tony. "System Identification Using Subspace-Based Instrumental Variable Methods." IFAC Proceedings Volumes 30, no. 11 (July 1997): 1069–74. http://dx.doi.org/10.1016/s1474-6670(17)42982-x.

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22

Takei, Yoshinori, Hidehito Nanto, Shunshoku Kanae, Zi-Jiang Yang, and Kiyoshi Wada. "Subspace-based identification methods using schur complement approach." IFAC Proceedings Volumes 36, no. 16 (September 2003): 879–84. http://dx.doi.org/10.1016/s1474-6670(17)34871-1.

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23

Lindquist, Anders, and Giorgio Picci. "On “Subspace Methods” Identification and Stochastic Model Reduction." IFAC Proceedings Volumes 27, no. 8 (July 1994): 693–99. http://dx.doi.org/10.1016/s1474-6670(17)47790-1.

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24

Ba, Teng Yue, Xi Qiang Guan, and Jian Wu Zhang. "Identification of Linear Tire Cornering Stiffness Using Subspace Methods." Applied Mechanics and Materials 701-702 (December 2014): 492–97. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.492.

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Abstract:

In this paper, subspace identification methods are proposed to estimate the linear tire cornering stiffness, which are only based on the road tests data without any prior knowledge. This kind of data-driven method has strong robustness. In order to validate the feasibility and effectiveness of the algorithms, a series of standard road tests are carried out. Comparing with different subspace algorithms used in road tests, it can be concluded that the front tire cornering stiffness can be estimated accurately by the N4SID and CCA methods when the double lane change test data are taken into analysis.

25

Shokravi, Hoofar, Hooman Shokravi, Norhisham Bakhary, Seyed Saeid Rahimian Koloor, and Michal Petrů. "A Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study." Applied Sciences 10, no. 9 (April 30, 2020): 3132. http://dx.doi.org/10.3390/app10093132.

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Abstract:

Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices’ performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC.

26

Lawal, Abdulmajid, Qadri Mayyala, Karim Abed-Meraim, Naveed Iqbal, and Azzedine Zerguine. "Toeplitz structured subspace for multi-channel blind identification methods." Signal Processing 188 (November 2021): 108152. http://dx.doi.org/10.1016/j.sigpro.2021.108152.

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27

Chiuso, Alessandro. "ASYMPTOTIC EQUIVALENCE OF CERTAIN CLOSED LOOP SUBSPACE IDENTIFICATION METHODS." IFAC Proceedings Volumes 39, no. 1 (2006): 297–302. http://dx.doi.org/10.3182/20060329-3-au-2901.00042.

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28

Zhao, Y., D. T. Westwick, and R. E. Kearney. "Subspace Methods for Identification of Human Ankle Joint Stiffness." IEEE Transactions on Biomedical Engineering 58, no. 11 (November 2011): 3039–48. http://dx.doi.org/10.1109/tbme.2010.2092430.

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29

Lang Tong and S. Perreau. "Multichannel blind identification: from subspace to maximum likelihood methods." Proceedings of the IEEE 86, no. 10 (1998): 1951–68. http://dx.doi.org/10.1109/5.720247.

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30

Chiuso, Alessandro, and Giorgio Picci. "PREDICTION ERROR VS SUBSPACE METHODS IN CLOSED LOOP IDENTIFICATION." IFAC Proceedings Volumes 38, no. 1 (2005): 506–11. http://dx.doi.org/10.3182/20050703-6-cz-1902.00085.

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31

Hu, Y., and S. Yurkovich. "Linear parameter varying battery model identification using subspace methods." Journal of Power Sources 196, no. 5 (March 2011): 2913–23. http://dx.doi.org/10.1016/j.jpowsour.2010.10.072.

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32

Viberg, M., B. Ottersten, B. Wahlberg, and L. Ljung. "Performance of Subspace Based State-Space System Identification Methods." IFAC Proceedings Volumes 26, no. 2 (July 1993): 63–66. http://dx.doi.org/10.1016/s1474-6670(17)48223-1.

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33

Bittanti, Sergio, and Marco Lovera. "Bootstrap-based estimates of uncertainty in subspace identification methods." Automatica 36, no. 11 (November 2000): 1605–15. http://dx.doi.org/10.1016/s0005-1098(00)00081-9.

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34

Scherrer, Wolfgang, and Christiaan Heij. "Identification of factor models by behavioural and subspace methods." Systems & Control Letters 32, no. 5 (December 1997): 335–44. http://dx.doi.org/10.1016/s0167-6911(97)00088-1.

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35

Jansson, Magnus, and Bo Wahlberg. "Counterexample to General Consistency of Subspace System Identification Methods." IFAC Proceedings Volumes 30, no. 11 (July 1997): 1573–78. http://dx.doi.org/10.1016/s1474-6670(17)43066-7.

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36

Picci, Giorgio, and Tohru Katayama. "Stochastic realization with exogenous inputs and ‘subspace-methods’ identification." Signal Processing 52, no. 2 (July 1996): 145–60. http://dx.doi.org/10.1016/0165-1684(96)00050-3.

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37

Westwick, David, and Michel Verhaegen. "Identifying MIMO Wiener systems using subspace model identification methods." Signal Processing 52, no. 2 (July 1996): 235–58. http://dx.doi.org/10.1016/0165-1684(96)00056-4.

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38

Chiuso, Alessandro, and Giorgio Picci. "Consistency analysis of some closed-loop subspace identification methods." Automatica 41, no. 3 (March 2005): 377–91. http://dx.doi.org/10.1016/j.automatica.2004.10.015.

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39

Luo, Xiaosuo, and Yongduan Song. "Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/869879.

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Abstract:

This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.

40

Heres, P. J., D. Deschrijver, W. H. A. Schilders, and T. Dhaene. "Combining Krylov subspace methods and identification-based methods for model order reduction." International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 20, no. 6 (2007): 271–82. http://dx.doi.org/10.1002/jnm.644.

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41

Moulines, E., P. Duhamel, J. F. Cardoso, and S. Mayrargue. "Subspace methods for the blind identification of multichannel FIR filters." IEEE Transactions on Signal Processing 43, no. 2 (1995): 516–25. http://dx.doi.org/10.1109/78.348133.

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42

Schrempf, Andreas, and Vincent Verdult. "IDENTIFICATION OF APPROXIMATIVE NONLINEAR STATE-SPACE MODELS BY SUBSPACE METHODS." IFAC Proceedings Volumes 38, no. 1 (2005): 934–39. http://dx.doi.org/10.3182/20050703-6-cz-1902.00157.

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43

TAKEI, Yoshinori, Jun IMAI, and Kiyoshi WADA. "Recursive Computation for Error Covariance Matrix in Subspace Identification Methods." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2000 (May 5, 2000): 19–24. http://dx.doi.org/10.5687/sss.2000.19.

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44

Juricek, Ben C., Dale E. Seborg, and Wallace E. Larimore. "Identification of the Tennessee Eastman Challenge Process with Subspace Methods." IFAC Proceedings Volumes 33, no. 15 (June 2000): 409–14. http://dx.doi.org/10.1016/s1474-6670(17)39785-9.

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45

Dahlén, Anders, Anders Lindquist, and Jorge Mari. "Experimental evidence showing that stochastic subspace identification methods may fail." Systems & Control Letters 34, no. 5 (July 1998): 303–12. http://dx.doi.org/10.1016/s0167-6911(98)00020-6.

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46

Zhang, Chengjin, and Robert R. Bitmead. "Multiple Antenna System Equalization Using Semi-Blind Subspace Identification Methods." IFAC Proceedings Volumes 36, no. 16 (September 2003): 591–96. http://dx.doi.org/10.1016/s1474-6670(17)34826-7.

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47

Ljung, Lennart. "Aspects and Experiences of User Choices in Subspace Identification Methods." IFAC Proceedings Volumes 36, no. 16 (September 2003): 1765–70. http://dx.doi.org/10.1016/s1474-6670(17)35015-2.

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48

Juricek, Ben C., Dale E. Seborg, and Wallace E. Larimore. "Identification of the Tennessee Eastman challenge process with subspace methods." Control Engineering Practice 9, no. 12 (December 2001): 1337–51. http://dx.doi.org/10.1016/s0967-0661(01)00124-1.

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49

Skullestad, A., and O. Hallingstad. "Identification of vibration parameters in a spacecraft using subspace methods." Control Engineering Practice 5, no. 4 (April 1997): 507–16. http://dx.doi.org/10.1016/s0967-0661(97)00030-0.

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50

Türkay, Semiha, and Hüseyin Akçay. "Road Roughness Evaluation by Curve-Fitting and Subspace-Identification Methods." Journal of Transportation Engineering 142, no. 11 (November 2016): 04016050. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000877.

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