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 theirparameters are empirically adjusted. Besides, the operation of these controllers relies on themeasurements provided by sensors located inside and near the greenhouse. If theinformation 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 functionproperly will impair the greenhouse operation. Therefore, an automatic diagnosis system offailures in greenhouses is proposed. The diagnosis system is based on deviations observedbetween measurements performed in the system and the predictions of a model of thefailure-free system. This comparison is done through a bank of fuzzy observers, where eachobserver 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 schemesfor adjusting the climate inside a greenhouse. The thesis employs the conventional Pseudo-Derivative Feedback (PDF) Controller. It develops the fuzzy PDF controller (FPDF). Thethesis also, develops two genetic algorithm (GA) based climatic control schemes, one isgenetic PDF (GPDF) and the other is genetic FPDF (GFPDF). The former uses GA toadjust the gains of the Pseudo-Derivative Feedback Controller (GPDF) and the later usesgenetic algorithm to optimize the FPDF controller parameters (i.e., scale factors and/orparameters of the membership functions). Finally, the thesis develops a fuzzy neural faultdetection and isolation system (FNFDIS), in which a bank of fuzzy observers are designedto 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 thedeveloped control schemes for controlling the greenhouse climate. The proposed schemesare tested through two experiments, setpoint tracking test and regulatory control test. Also,the proposed diagnostic system was tested through four experiments. Compared with theresults obtained using the conventional controllers, best results have been achieved usingthe proposed control schemes.

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 theirparameters are empirically adjusted. Besides, the operation of these controllers relies on themeasurements provided by sensors located inside and near the greenhouse. If theinformation 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 functionproperly will impair the greenhouse operation. Therefore, an automatic diagnosis system offailures in greenhouses is proposed. The diagnosis system is based on deviations observedbetween measurements performed in the system and the predictions of a model of thefailure-free system. This comparison is done through a bank of fuzzy observers, where eachobserver 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 schemesfor adjusting the climate inside a greenhouse. The thesis employs the conventional Pseudo-Derivative Feedback (PDF) Controller. It develops the fuzzy PDF controller (FPDF). Thethesis also, develops two genetic algorithm (GA) based climatic control schemes, one isgenetic PDF (GPDF) and the other is genetic FPDF (GFPDF). The former uses GA toadjust the gains of the Pseudo-Derivative Feedback Controller (GPDF) and the later usesgenetic algorithm to optimize the FPDF controller parameters (i.e., scale factors and/orparameters of the membership functions). Finally, the thesis develops a fuzzy neural faultdetection and isolation system (FNFDIS), in which a bank of fuzzy observers are designedto 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 thedeveloped control schemes for controlling the greenhouse climate. The proposed schemesare tested through two experiments, setpoint tracking test and regulatory control test. Also,the proposed diagnostic system was tested through four experiments. Compared with theresults obtained using the conventional controllers, best results have been achieved usingthe proposed control schemes.