Nonlinear system identification using heterogeneous multiple models

• Autorzy: Rodolfo Orjuela , Benoît Marx , José Ragot , Didier Maquin
• Języki publikacji: EN

Abstrakty

EN

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to that of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with off-line identification of nonlinear systems based on heterogeneous multiple models. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called the no output tracking effect. The origin of this difficulty is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of the model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

Słowa kluczowe

EN

nonlinear system identification; multiple models; heterogeneous submodels

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2013
• Tom: 23
• Numer: 1
• Strony: 103-115

Daty

wydano

2013

otrzymano

2012-01-19

poprawiono

2012-06-13

poprawiono

2012-10-17

Twórcy

autor

Rodolfo Orjuela

• Modelling Intelligence Process and System Laboratory, MIPS, EA 2332, University of Haute-Alsace, 12 rue des frères Lumière, F-68093 Mulhouse, France

autor

Benoît Marx

• CRAN, UMR 7039, University of Lorraine, 2 Av. de la forêt de Haye, F-54516 Vandœuvre-lès-Nancy, France
• CRAN, UMR 7039, CNRS, France

autor

José Ragot

• CRAN, UMR 7039, University of Lorraine, 2 Av. de la forêt de Haye, F-54516 Vandœuvre-lès-Nancy, France
• CRAN, UMR 7039, CNRS, France

autor

Didier Maquin

• CRAN, UMR 7039, University of Lorraine, 2 Av. de la forêt de Haye, F-54516 Vandœuvre-lès-Nancy, France
• CRAN, UMR 7039, CNRS, France

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Identyfikatory

DOI

10.2478/amcs-2013-0009