Inhaltsangabe
Master's Thesis from the year 2005 in the subject Computer Sciences - Artificial Intelligence, grade: MSc, , course: Intelligent Control, language: English, abstract: [...] In practice, conventional controllers were used to control the system however their
parameters are empirically adjusted. Besides, the operation of these controllers relies on the
measurements provided by sensors located inside and near the greenhouse. If the
information provided by one or several of these sensors is erroneous, the controllers will not operate properly. Similarly, failure of one or several of the actuators to function
properly will impair the greenhouse operation. Therefore, an automatic diagnosis system of
failures in greenhouses is proposed. The diagnosis system is based on deviations observed
between measurements performed in the system and the predictions of a model of the
failure-free system. This comparison is done through a bank of fuzzy observers, where each
observer becomes active to a specific failure signature and inactive to the other failures.
Neural networks are used to develop a model for the failure-free greenhouse.
The main objective of this thesis is to explore and develop intelligent control schemes
for adjusting the climate inside a greenhouse. The thesis employs the conventional Pseudo-
Derivative Feedback (PDF) Controller. It develops the fuzzy PDF controller (FPDF). The
thesis also, develops two genetic algorithm (GA) based climatic control schemes, one is
genetic PDF (GPDF) and the other is genetic FPDF (GFPDF). The former uses GA to
adjust the gains of the Pseudo-Derivative Feedback Controller (GPDF) and the later uses
genetic algorithm to optimize the FPDF controller parameters (i.e., scale factors and/or
parameters of the membership functions). Finally, the thesis develops a fuzzy neural fault
detection and isolation system (FNFDIS), in which a bank of fuzzy observers are designed
to detect faults that may occur in the greenhouse end items (e.g.., sensors and actuators).
Simulation experiments are performed to test the soundness and capabilities of the
developed control schemes for controlling the greenhouse climate. The proposed schemes
are tested through two experiments, setpoint tracking test and regulatory control test. Also,
the proposed diagnostic system was tested through four experiments. Compared with the
results obtained using the conventional controllers, best results have been achieved using
the proposed control schemes.
Reseña del editor
Master's Thesis from the year 2005 in the subject Engineering - Artificial Intelligence, grade: MSc, , course: Intelligent Control, language: English, abstract: [...] In practice, conventional controllers were used to control the system however their parameters are empirically adjusted. Besides, the operation of these controllers relies on the measurements provided by sensors located inside and near the greenhouse. If the information provided by one or several of these sensors is erroneous, the controllers will not operate properly. Similarly, failure of one or several of the actuators to function properly will impair the greenhouse operation. Therefore, an automatic diagnosis system of failures in greenhouses is proposed. The diagnosis system is based on deviations observed between measurements performed in the system and the predictions of a model of the failure-free system. This comparison is done through a bank of fuzzy observers, where each observer becomes active to a specific failure signature and inactive to the other failures. Neural networks are used to develop a model for the failure-free greenhouse. The main objective of this thesis is to explore and develop intelligent control schemes for adjusting the climate inside a greenhouse. The thesis employs the conventional Pseudo- Derivative Feedback (PDF) Controller. It develops the fuzzy PDF controller (FPDF). The thesis also, develops two genetic algorithm (GA) based climatic control schemes, one is genetic PDF (GPDF) and the other is genetic FPDF (GFPDF). The former uses GA to adjust the gains of the Pseudo-Derivative Feedback Controller (GPDF) and the later uses genetic algorithm to optimize the FPDF controller parameters (i.e., scale factors and/or parameters of the membership functions). Finally, the thesis develops a fuzzy neural fault detection and isolation system (FNFDIS), in which a bank of fuzzy observers are designed to detect faults that m
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