Friday , September 21 2018

An Optimal Instrumental Variable Identification Approach for Left Matrix Fraction Description Models

Mohamed AKROUM, Kamel HARICHE
Department of Electrical and Electronic Engineering
M’hamed Bougara University, 35000 Avenue de l’independence, Boumerdes, Algeria

Abstract: The main contribution of this paper is the extension of the Simplified Refined Instrumental Variable (SRIV) identification algorithm for SISO systems to the identification of 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.

Keywords: Multivariable System Identification, SRIV, LMFD, IV4, Steiglitz-McBride.

Mohamed Akroum received his Electrical Engineer degree in 1995 from M’hamed Bougara University and his Magister degree in 1998 from Houari Boumedienne University of Sciences and Technology. He worked in the Advanced Technologies Development Center in Algiers for two years and He is currently a Lecturer at M’hamed Bougara University and he is preparaing a Ph.D degree since 2003. His current research interests include MIMO system identification and multivariable automatic control.

Kamel Hariche received his M.Sc. and PhD. degrees from the University of Houston (USA) in 1978 and 1987 respectively. He is currently a Professor at M’hamed Bougara University. His research is related to linear and nonlinear systems as well as Automatic control of MIMO systems.

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CITE THIS PAPER AS:
Mohamed AKROUM, Kamel HARICHE, An Optimal Instrumental Variable Identification Approach for Left Matrix Fraction Description Models, Studies in Informatics and Control, ISSN 1220-1766, vol. 17 (4), pp. 361-372, 2008.