Self – Tuning Method of Fuzzy System: An Application on Greenhouse Process
Authors: M. Massour El Aoud, M. Franceschi, M. Maher
Abstract:
The approach proposed here is oriented in the direction of fuzzy system for the analysis and the synthesis of intelligent climate controllers, the simulation of the internal climate of the greenhouse is achieved by a linear model whose coefficients are obtained by identification. The use of fuzzy logic controllers for the regulation of climate variables represents a powerful way to minimize the energy cost. Strategies of reduction and optimization are adopted to facilitate the tuning and to reduce the complexity of the controller.
Keywords: Greenhouse, fuzzy logic, optimization, gradient descent.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1326724
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[1] J. Duplaix, Implantation et validation d-une identification en ligne pour serre agricole, DEA Informatique et automatique, Université de Marseille III, (1991).
[2] INRA, conference acts of AIP Intersectorielle, greenhouse -ALENYA (1996).
[3] Y. Zhang, Y. Mahrer, M. Margolin, Predicting the microclimate inside a greenhouse: an application of a one-dimensional numerical model in an unheated greenhouse, Agricultural and forest meteorology (1997).
[4] G.P.A. Bot, Greenhouse climate: From physical processes to a dynamic model, Ph.D. Thesis, Agricultural University, Wagemingen, Netherlands, (1983).
[5] T. Boulard, J. F. Meneses, M. Mermier, G. Papadakis, The mechanisms Involved in the natural ventilation of greenhouses, Agricultural and forest meteorology (1995).
[6] T. Boulard, B. DRAOUI, Natural ventilation of greenhouse with continuous roof vents measurement and data analysis, Journal of Agricultural engineering research (1995).
[7] L. Oueslati, a quadratic multivaraible control of greenhouse. Thesis of Toulon University (1990).
[8] C. Boaventura, C. Couto, A. E. Ruano, Real - time parameter estimation of dynamic temperature models for greenhouse environmental control, Elsevier (1997)
[9] A. Trabelsi, F. Lafont, M. Kamoun, G. Enea, Identification of nonlinear multivariable systems by adaptive fuzzy Takagi-sugeno model, International journal of computational cognition September (2004).
[10] S. Oh W. Pedrycz, Identification of fuzzy systems by means of an autotuning algorithm and its application to non linear systems, Fuzzy sets and Systems (2000).
[11] F. Lafont, J.-F.Balmat, Optimized fuzzy control of greenhouse, Journal of fuzzy sets and systems (2002).
[12] F. Lafont, J.-F.Balmat,Fuzzy logic to the identification and the command of the multidimensional systems, International journal of computational cognition September (2004).
[13] R. Caponetto, L. Fortuna G. Nunnari, L. Occhipinti, M. G. Xibilia, Soft computing for greenhouse climate control, Journal of IEEE Transactions on fuzzy systems (2000).
[14] V. Lacrose, complexity reduction of fuzzy controllers: application to the multivariable control. Thesis of INSA Toulouse (1997).
[15] M. Maher, on the modelling, the evaluation, the identification and the control of a bio process Thesis of Rabat university (1996
[16] J. S. Gibson, G. H. , C. F. Wu, Least-squares estimation of input/output models for distributed linear systems in the presence of noise, Journal of Automatica (2000).
[17] M. Massour, M. Franceschi, M. Maher, Multivariable Control for Greenhouse Climate, Computational Engineering in System Application (CESA) Ecole Centrale de Lille France (2003).
[18] M. Massour, M. Franceschi, M. Maher Identification and control of the microclimate inside a greenhouse Physical Maghrebin journal (2006)
[19] Y.Shi, M. Mizumoto. Some considerations on conventional neuro-fuzzy learning algorithms by gradient descent method. Fuzzy Sets and Systems (2000)
[20] C. Viard Gaudin, Simulation and auto-adaptive control of a greenhouse, Thesis of Nantes University (1981).
[21] A. Bekkaoui, a simplified dynamic modelling of the greenhouse climate thesis of Gembloux Agronomy University (1998).
[22] Y. Shi, M. Mizumoto, An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules, Fuzzy Sets and Systems (2001) 339- 350.