Identification of 2D Roesser models by using linear fractional transformations

Mohamed Farah; Guillaume Mercere; Regis Ouvrard; Thierry Poinot; Jose Ramos

doi:10.1109/ECC.2014.6862307

Identification of 2D Roesser models by using linear fractional transformations

Mohamed Farah
, Guillaume Mercere
, Regis Ouvrard
, Thierry Poinot
, Jose Ramos

Nova Southeastern University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, the problem of identifying a 2D linear time-invariant Roesser model is tackled. Based on the strong relation between the linear fractional representation and the nD Roesser model, a gradient-based optimization algorithm is suggested to estimate the state-space matrices of a standard Roesser model in the black-box as well as the gray-box model identification frameworks. Contrary to the developments available in the literature, no specific restriction (to the 2D causal, recursive and separable-in-denominator (CRSD) state-space models) is required by the non-linear programming technique developed in this article. The efficiency of this method is illustrated through two simulation examples: a CRSD state-space model and a 2D Roesser model of a co-current flow heat exchanger.

Original language	English
Title of host publication	2014 European Control Conference, ECC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	382-387
Number of pages	6
ISBN (Electronic)	9783952426913
DOIs	https://doi.org/10.1109/ECC.2014.6862307
State	Published - Jul 22 2014
Event	13th European Control Conference, ECC 2014 - Strasbourg, France Duration: Jun 24 2014 → Jun 27 2014

Publication series

Name	2014 European Control Conference, ECC 2014

Conference

Conference	13th European Control Conference, ECC 2014
Country/Territory	France
City	Strasbourg
Period	6/24/14 → 6/27/14

Bibliographical note

Publisher Copyright:
© 2014 EUCA.

ASJC Scopus Subject Areas

Control and Systems Engineering

Access to Document

10.1109/ECC.2014.6862307

Cite this

Identification of 2D Roesser models by using linear fractional transformations. / Farah, Mohamed; Mercere, Guillaume; Ouvrard, Regis et al.
2014 European Control Conference, ECC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 382-387 6862307 (2014 European Control Conference, ECC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Farah, M, Mercere, G, Ouvrard, R, Poinot, T & Ramos, J 2014, Identification of 2D Roesser models by using linear fractional transformations. in 2014 European Control Conference, ECC 2014., 6862307, 2014 European Control Conference, ECC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 382-387, 13th European Control Conference, ECC 2014, Strasbourg, France, 6/24/14. https://doi.org/10.1109/ECC.2014.6862307

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N2 - In this paper, the problem of identifying a 2D linear time-invariant Roesser model is tackled. Based on the strong relation between the linear fractional representation and the nD Roesser model, a gradient-based optimization algorithm is suggested to estimate the state-space matrices of a standard Roesser model in the black-box as well as the gray-box model identification frameworks. Contrary to the developments available in the literature, no specific restriction (to the 2D causal, recursive and separable-in-denominator (CRSD) state-space models) is required by the non-linear programming technique developed in this article. The efficiency of this method is illustrated through two simulation examples: a CRSD state-space model and a 2D Roesser model of a co-current flow heat exchanger.

AB - In this paper, the problem of identifying a 2D linear time-invariant Roesser model is tackled. Based on the strong relation between the linear fractional representation and the nD Roesser model, a gradient-based optimization algorithm is suggested to estimate the state-space matrices of a standard Roesser model in the black-box as well as the gray-box model identification frameworks. Contrary to the developments available in the literature, no specific restriction (to the 2D causal, recursive and separable-in-denominator (CRSD) state-space models) is required by the non-linear programming technique developed in this article. The efficiency of this method is illustrated through two simulation examples: a CRSD state-space model and a 2D Roesser model of a co-current flow heat exchanger.

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Identification of 2D Roesser models by using linear fractional transformations

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Access to Document

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Cite this