Self – Tuning Method of Fuzzy System: An Application on Greenhouse Process
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.
[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.
[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.
@article{"International Journal of Biological, Life and Agricultural Sciences:56974", author = "M. Massour El Aoud and M. Franceschi and M. Maher", title = "Self – Tuning Method of Fuzzy System: An Application on Greenhouse Process", 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.", volume = "1", number = "7", pages = "81-5", }
