An agricultural greenhouse is a complex and Multi-Input Multi-Output MIMO system in which the internal parameters create a favorable microclimate for agricultural production. Temperature and internal humidity are two parameters that have a major impact on greenhouse yield. The objective of this study was to propose a simulated dynamic model in a MATLAB/Simulink environment for experimental validation. Moreover, a fuzzy controller was designed to manage a greenhouse indoor climate by means of an asynchronous motor for ventilation, heating, humidification, etc. An intelligent system to control these actuators for an optimal inside climate was implemented in the model. The dynamic model was validated by comparing the simulation results to experimental measurements. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate. Finally, a photovoltaic generator was modeled, with the aim of reducing the costs of agricultural production. It feeds the asynchronous motor with a vector control optimized by fuzzy logic that drives a variable speed fan.

Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.

This paper presents the modeling and simulation of a Multi-Source Power System (MSPS)—composed of two renewable energy sources and supported by a Battery Energy Storage System (BESS)—to supply the ventilation and heating system for a temperature-controlled agricultural greenhouse. The first one is a photovoltaic (PV) generator connected to a DC/AC inverter and the second one is a wind turbine connected to a Permanent Magnet Synchronous Generator (PMSG). The temperature contribution in the model of the PV generator is deeply studied. A Maximum Power Point Tracking (MPPT) control based on fuzzy logic is used to drive a SEPIC converter to feed the maximum power to the greenhouse actuators. The operation of the actuators (ventilation and heating systems), on the basis of the mismatch between the internal temperature and the reference one, is controlled by a PI controller optimized by fuzzy logic, for more robust results. The simulation of the system is carried out in a Matlab/Simulink environment and its validation is based on the comparison between the simulated and experimental data for a test greenhouse, located in the Faculty of Science in Tunis. The results show that the proposed system provides an efficient solution for controlling the microclimate of the agricultural greenhouse in different periods of the year.

Due to their small dimension and isolated energy systems, islands face a significant energy supply challenge. In general, they use fossil fuels for electricity generation. Fossil fuels are a major source of CO2 emissions, and they are very costly. The cost of electricity generation on islands is up to 10 times higher than on the mainland. This situation without a doubt represents a financial burden for the islanders. Using renewable sources, especially solar and wind sources, offers great potential for power generation in remote locations, as they are a clean and inexhaustible source of energy. Electrifying these zones with a hybrid system consisting of a photovoltaic (PV) and wind systems associated to a hydraulic and an electrochemical storage system is a promising alternative. The purpose of this study is to optimize the dimension of the components generation of systems, especially for a remote island in Tunisia. The first part of this object outlines the PV-wind-battery-hydraulic generation system architecture and modeling. The optimal sizing of the device additives, satisfying two criteria with the aid of evolutionary algorithms NSGAII, is defined inside the second part of this article. The outcomes are discussed from the point of view of the importance of the system dimension and in terms of compliance with the criteria through the study of the most optimal particular configurations.

For several decades, many countries have favored irrigation as a means of regulating, diversifying, and increasing agricultural production to meet the growing domestic demand for food, and even to generate exportable surpluses. As with most Mediterranean countries, Tunisia has inherited a long tradition in irrigation; thus, the management of the scarcity of water resources poses a very important challenge that is gradually increasing due to the effects of climate change undergone by the region and confronting the agricultural sector. Aiming at a new model of sustainable development, ensuring the optimization of water resources management, as well as the protection of natural resources and the environment, this work proposed the modern design of a photovoltaic pumping chain dedicated for drip irrigation, which is controlled using an intelligent neuron-fuzzy controller with an ANFIS architecture and implemented on a Raspberry Pi platform. Thanks to this design, the efficiency of the pumping chain increased exponentially to a value of approximately 95%, achieving water pumping optimization while exploiting renewable energy resources, thus guaranteeing the longevity of water resources, as well as the continuity of diversified agricultural production.