Technical Brief

An Experimental Validation of a New Method for Multimodel Identification

[+] Author and Article Information
Anis Messaoud

Department of Electrical Engineering,
National School of Engineers of Gabes,
University of Gabes Tunisia,
Gabes 6029, Tunisia
e-mail: messaoud_anis@yahoo.fr

Ridha Ben Abdennour

Department of Electrical Engineering,
National School of Engineers of Gabes,
University of Gabes Tunisia,
Gabes 6029, Tunisia
e-mail: ridha.benabdennour@enig.rnu.tn

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 3, 2016; final manuscript received July 14, 2017; published online October 6, 2017. Assoc. Editor: Yang Shi.

J. Dyn. Sys., Meas., Control 140(2), 024502 (Oct 06, 2017) (7 pages) Paper No: DS-16-1480; doi: 10.1115/1.4037530 History: Received October 03, 2016; Revised July 14, 2017

In this paper, we propose a new method for an optimal systematic determination of models' base for multimodel identification. This method is based on the neural classification of data set picked out on a considered nonlinear system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models' base. A simulation example and an experimental validation on a chemical reactor are presented to evaluate the effectiveness of the proposed method.

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Fig. 1

Data set for the system identification

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Fig. 2

Weighting functions evolutions (classical choice based on the static characteristic)

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Fig. 4

Data classification

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Fig. 3

Evolutions of the real and the multimodel outputs (classical technique)

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Fig. 5

Weighting functions evolutions (proposed method)

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Fig. 10

Evolutions of weighting functions

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Fig. 6

Evolutions of the multimodel and the real outputs (proposed approach)

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Fig. 7

The chemical reactor

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Fig. 8

Identification data set

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Fig. 9

Data classification

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Fig. 11

Evolutions of the multimodel output yMM(k) and the reactor output y(k)

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Fig. 12

The evolution of the relative error (proposed approach)



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