Abstract
This paper presents a design and a real time application of an efficient adaptive neural-fuzzy inference system to regulate the climate inside the greenhouse. Basically, the adaptive neural-fuzzy inference system is a combination of fuzzy logic and artificial neural network techniques. The proposed control improves the capacity to track the temperature and relative humidity reference which is required for the crops growth. The adaptive neural-fuzzy inference system training datasets are extracted from the fuzzy logic controller model developed in MATLAB Simulink and its robustness has been verified experimentally. This technique presents good performances in terms of set point tracking, the response time and the robustness with respect to external parameters variation, non-linearity and the energy optimization.
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