Fuzzy identification of a greenhouse

  • Authors:

  • Amine Trabelsi;Frederic Lafont;Mohamed Kamoun;Gilles Enea

  • Affiliations:

  • Unité de Commande Automatique, Ecole Nationale d'ngénieurs de Sfax, B.P.W, 3038 Sfax, Tunisie, France;Laboratoire LSIS, UMR CNRS 6168, Université du Sud-Toulon-Var, B.P 20132, 83957 La Garde Cedex 20, France;Unité de Commande Automatique, Ecole Nationale d'ngénieurs de Sfax, B.P.W, 3038 Sfax, Tunisie, France;Laboratoire LSIS, UMR CNRS 6168, Université du Sud-Toulon-Var, B.P 20132, 83957 La Garde Cedex 20, France

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2007

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Abstract

Nonlinear dynamic systems' modelling is difficult. The solutions proposed are generally based on the linearization of the process behaviour around the operating points. Other researches were carried out on this technique of linearization not only around the operating points, but also in all the input-output space allowing the obtaining of several local linear models. The major difficulty with this technique is the model transition. Fuzzy logic makes it possible to solve this problem thanks to its properties of universal approximator. Indeed, many techniques of modelling and identification based on fuzzy logic are often used for this type of systems. Among these techniques, we find those based on the fuzzy clustering technique. The proposed method uses in a first stage the fuzzy clustering technique to determine both the premises and the consequent parameters of the fuzzy Takagi-Sugeno rules. In a second stage these consequent parameters are adapted by using the recursive weighted least squares algorithm with a forgetting factor. We will try in this paper to apply this method to model the air temperature and humidity inside the greenhouse.