New Approach for Estimating the Input Fault Based on the Sliding Window Identification Technique of the SISO ARX-Laguerre Multimodel

Hajer BENAMOR¹*, Marwa YOUSFI¹, Larbi CHRIFI ALAOUI², Hassani MESSAOUD¹
¹ Laboratory of Automatic, Signal and Image Processing, Department of Electrical Engineering,
School of Engineers Monastir-Tunisia, Ibn El Jazzar 5019 Monastir, Tunisia
hageramor@gmail.com (*Corresponding author), yousfimarwa90@gmail.com,
assani.messaoud@enim.rnu.tn
² Laboratory of Innovative Technology (LTI-UR-UPJV 3899), University of Picardie Jules Verne,
80000 Amiens, France
larbi.alaoui@u-picardie.fr

Abstract: This article focuses on developing a new approach for diagnosing of nonlinear systems. This approach consists in estimating the input fault by using the proposed ARX-Laguerre multimodel (Adaily, 2018) in its decoupled structure. This method is processed in two consecutive steps. The first consists in the offline identification of parameters of the ARX-Laguerre multimodel (weighting functions, Laguerre poles and -Fourier coefficients). The second step proposes an input fault estimation algorithm based on the online update of the parameters using the sliding window principle. This proposed diagnosis approach is implemented on a Continuously Stirred Reactor (CSTR) Benchmark to estimate the input fault. A comparative study regarding the results provided by a Proportional Integral (PI) observer based on the decoupled ARX-Laguerre multimodel is performed to attest this new approach.

Keywords: Multimodel, ARX Laguerre, Genetic Algorithm, Parameter optimisation, Sliding window, Input fault estimation, PI observer.

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CITE THIS PAPER AS:
Hajer BENAMOR, Marwa YOUSFI, Larbi CHRIFI ALAOUI, Hassani MESSAOUD, New Approach for Estimating the Input Fault Based on the Sliding Window Identification Technique of the SISO ARX-Laguerre Multimodel, Studies in Informatics and Control, ISSN 1220-1766, vol. 32(4), pp. 115-127, 2023. https://doi.org/10.24846/v32i4y202311