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Journal of Control Science and Engineering - Volume20162016, Article ID9614167, 12 pages -

Research ArticleUniversity of Sfax, National Engineering School of Sfax ENIS, Laboratory of Sciences and Technique of Automatic Control and Computer Engineering Lab-SAT, BP 1173, 3038 Sfax, Tunisia

Received 29 November 2015; Revised 20 April 2016; Accepted 12 May 2016

Academic Editor: JamesLam

Copyright 2016 Houda Salhi and Samira Kamoun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper deals with the parameter estimation problem for multivariable nonlinear systems described by MIMO state-space Wiener models. Recursive parameters and state estimation algorithms are presented using the least squares technique, the adjustable model, and the Kalman filter theory. The basic idea is to estimate jointly the parameters, the state vector, and the internal variables of MIMO Wiener models based on a specific decomposition technique to extract the internal vector and avoid problems related to invertibility assumption. The effectiveness of the proposed algorithms is shown by an illustrative simulation example.





Autor: Houda Salhiand Samira Kamoun

Fuente: https://www.hindawi.com/



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