Browse > Article
http://dx.doi.org/10.5370/JEET.2009.4.1.135

Extending the SRIV Identification Algorithm to MIMO LMFD Models  

Akroum, Mohamed (Dept. of Electrical and Electronic Engineering, M'hamed Bougard University)
Hariche, Kamel (Dept. of Electrical and Electronic Engineering, M'hamed Bougard University)
Publication Information
Journal of Electrical Engineering and Technology / v.4, no.1, 2009 , pp. 135-142 More about this Journal
Abstract
In this paper the Simplified Refined Instrumental Variable (SRIV) identification algorithm for SISO systems is extended to MIMO systems described by a Left Matrix Fraction Description (LMFD). The performance of the extended algorithm is compared to the well-known MIMO four-step instrumental variable (IV4) algorithm. Monte Carlo simulations for different signal to noise ratios are conducted to assess the performance of the algorithm. Moreover, the algorithm is applied to a simulated quadruple tank process.
Keywords
MIMO system identification; SRIV; LMFD; IV4; Steiglitz-McBride;
Citations & Related Records

Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 L. Ljung, System identification toolbox for Matlab, Version 6.1.2, www.mathworks.com. 2005
2 P. Young, and AJ. Jakeman, Refined instrumental variable methods of time-series analysis, International Journal of Control 29, 621-644,1979   DOI   ScienceOn
3 P. Young, An instrumental variable approach to ARMA model identification and estimation, Proceedings of the 14th IF AC Symposium on System identification (SYSID'2006 Newcastle, Australia), 2006
4 K. Steiglitz and. L.E. McBride, A technique for the identification of linear systems, IEEE Transactions on Automatic Control, 10,461-464, 1965   DOI
5 P. Stoica, and T. Soderstrom, 1981, The Steiglitz - McBride identification algorithm revisited, IEEE Transactions on Automatic Control, 29, 712-719
6 K.H. Johansson, The Quadruple-Tank Process: A multivariable laboratory process with an adjustable zero, IEEE Transactions on Control Systems Technology, Vol.8, No.3, May 2000
7 S.D. Fassois, MIMO LMS-ARMAX identification of vibrating structures, Mechanical Systems and Signal Processing., 15,723-735,2001   DOI   ScienceOn
8 A. Nehorai, and M. Morf, Recursive identification Algorithms for Right Matrix Fraction Description models, IEEE Transactions on Automatic Control, 29, 1103-1106, 1984   DOI
9 T. Kailath, Linear Systems, Prentice Hall, 1980
10 S.Kim, J.Jin, Y.Park, Approximate ML Detection with Best channel Matrix Selection for MIMO systems, Journal of Electrical Engineering & Technology, Vol.3, No.2, June 2008
11 H. Zabot and K. Hariche, On solvents-based model reduction ofMIMO systems, International journal of systems science, 28, 499-505, 1997   DOI   ScienceOn
12 L. Ljung, System Identification: Theory for the user, Prentice Hall, 1999