A Kind of Frequency Subspace Identification Method with Time Delay and Its Application in Temperature Modeling of Ceramic Shuttle Kiln
American Journal of Computer Science and Technology
Volume 1, Issue 4, December 2018, Pages: 85-89
Received: Nov. 9, 2018;
Accepted: Dec. 20, 2018;
Published: Jan. 22, 2019
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Zhu Yonghong, School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen City, China
Yu Yuanjun, School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen City, China
Wang Jianhong, School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou City, China
In this paper, a problem in engineering area which the output variables are not corresponding to input variables is presented. To improve it, a kind of method to the identification and modeling of a common linear state system with delay factor are studied. The domain of this system with time-delay factor is transformed from the time-domain to the frequency-domain firstly, and then the subspace identification model with the hiding delay factor is constructed by using the data of frequency domain response. The coefficient matrix of the constructed model is identified by using the principal component analysis. And the engineering system can be modeled by knowing the state matrices in time domain which can be extracted from the coefficient matrices and using the least squares method from the frequency domain. On this basis, the time-delay factor of original system is split from input matrix by a kind of separated method. At last, the method proposed is used to identify the temperature system model of ceramic shuttle kiln. Simulation results show that the proposed method is effective and feasible.
A Kind of Frequency Subspace Identification Method with Time Delay and Its Application in Temperature Modeling of Ceramic Shuttle Kiln, American Journal of Computer Science and Technology.
Vol. 1, No. 4,
2018, pp. 85-89.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
L. Ljung. System identification: theory for the user. 1999. Prerbtice Hall.
Z. Ye, M. T. Kai, M. Carman, Grouping points by shared subspaces for effective subspace clustering, Pattern Recognition, 2018, 83: 230-244.
C. Priori, M. De Angelis, R. Betti, On the selection of user-defined parameters in data-driven stochastic subspace identification, Mechanical Systems and Signal Processing, 2018, 100: 501-523.
X. S. Luo, Y. D. Song, Data-driven predictive control of Hammerstein–Wiener systems based on subspace identification, Information Sciences, 2018, 422:447-461
L. Zhang, V. Lieven, Interior-point method for nuclear norm approximation with application to system identification, SIAM Journal on Matrix Analysis and Applications, 2009, 31(3): 1235-1256.
M. G. Plessen, T. A. Wood, R. S. Smith, Time-domain Subspace Identification Algorithms using Nuclear Norm Minimization, IFAC-Papers Online, 2015, 48(28): 903-908.
S. T. Navalkar, J. W. van Wingerden, Nuclear Norm-Based Recursive Subspace Identification for Wind Turbine Flutter Detection, IEEE Transactions on Control Systems Technology, 2017, 99:1-13.
Y. Gu, R. Ding, A least squares identification algorithm for a state space model with multi-state delays, Applied Mathematics Letters, 2013, 26(7): 748-753.
J. H. Wang, D. B. Wang, Z. S. Wang, Forgetting Factor Algorithm for Aircraft Flutter Modal Parameter Identification, Chinese Space Science and Technology 2009, (06): 7-14.
W. Li, C. Peng, Y. Wang, Frequency Domain Subspace Identification of Fractional Order Time Delay System, International Journal of Control, Automation and Systems, 2011, 9(2): 310–316.
T. McKelvey, H. Akcay, L. Ljung, Subspace-based multivariable system identification from frequency response data, IEEE Transactions on Automatic Control, 1996, 41(7): 960-979.
B. Kim, C. Y. Ko, N. Wong, Tensor network subspace identification of polynomial state space models, Automatica, 2018, 95: 187-196.
B. Telsang, S. T. Navalkar, J. W. van Wingerden, Recursive Nuclear Norm based Subspace Identification, IFAC-Papers Online, 2017, 50(1): 9490-9495.
Oveisi, T. Nestorović, A. Montazeri, Frequency Domain Subspace Identification of Multivariable Dynamical Systems for Robust Control Design, IFAC-Papers Online, 2018, 51(15): 990-995.