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Start date: 2020Subject: search.filters.subject.Fuzzy logic

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    Modern artificial intelligence technics for unmanned aerial vehicles path planning and control
    (2025) Zamoum, Yasmine; Baiche, Karim; Benkeddad, Youcef; Bouzida, Brahim
    Unmanned aerial vehicles (UAVs) require effective path planning algorithms to navigate through complex environments. This study investigates the application of Deep Q-learning and Dyna Q-learning methods for UAV path planning and incorporates fuzzy logic for enhanced control. Deep Q-learning, a reinforcement learning technique, employs a deep neural network to approximate Q-values, allowing the UAV to improve its path planning capabilities by maximizing cumulative rewards. Conversely, Dyna Q-learning leverages simulated scenarios to update Q- values, refining the UAV’s decision-making process and adaptability to dynamic environments. Additionally, fuzzy logic control is integrated to manage UAV movements along the planned path. This control system uses linguistic variables and fuzzy rules to handle uncertainties and imprecise information, enabling real-time adjustments to speed, altitude, and heading for accurate path following and obstacle avoidance. The research evaluates the effectiveness of these methods individually, with a focus on model-free learning in a gradual training approach, and compares their performance in terms of path planning accuracy, adaptability, and obstacle avoidance. The paper contributes to a deeper understanding of UAV path planning techniques and their practical applications in various scenarios.
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    Design of an Advanced Optimal Fuzzy Controller For a Binary Distillation Column
    (Université M'hamed Bougara de Boumerdès, 2024) Bendib, Riad; Mechhoud, El-arkam; Bentarzi, Hamid; Batout, Naoual
    The most common control philosophy followed in the chemical process industries is the SISO system using the conventional PID controller algorithms. One drawback is relying on models for both control and design work in Chemical process industries (CPI) is that many problems are very complex and accurate models are difficult, if not impossible to obtain. To overcome these problems, it will be helpful to apply techniques that use human judgment and experience rather than precise mathematical models, which in the major cases deduced from the linearization of the system and simplification hypothesis. The fuzzy logic systems are capable of handling complex, nonlinear systems using simple solutions. However, obtaining an optimal set of fuzzy membership functions is not an easy task. In this chapter a solution based on artificial intelligence is proposed to improve the control of a binary distillation column. The solution is based on the use of the advantages of both fuzzy logic and genetic algorithms. The fuzzy logic is used as a supervisory PI controller that is a simple PI controller that generally used in controlling distillation columns with parameters deduced from the fuzzy supervisor. The membership functions shape is deduced by using research algorithms based on hierarchical genetic algorithms. The results show that the Fuzzy supervisory PI controller provide an excellent tracking toward set point change
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    Heuristic and learning method for obstacle avoidance with mobile robot
    (IEEE, 2020) Lachekhab, Fadhila; Acheli, Dalila; Tadjine, Mohamed; Meraihi, Yassine
    In this paper, a fuzzy controller obstacle avoidance of the mobile robot Pioneer II is proposed. The fuzzy inference system FIS of this controller is performed by two methods: heuristic and reinforcement learning. the manual tuning of the fuzzy control system can be long and difficult. In contrast, reinforcement learning has proven theoretically and practically its ability to automatically optimize some parameters of the FIS. For that, the Fuzzy Actor-Critic Learning algorithm allows the determination of the parameters of the conclusions among of an available set fixed by the operator. The proposed algorithm allows the automatic determination of the parameters of the conclusions of the fuzzy rules. The simulations show that the two controllers (heuristic, RL controller) are able to avoid the different shapes of obstacles contained in known environments, and they show exceptionally good robustness when changing the environment (shape of obstacles, location of obstacles in the environment
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    Strategy for Optimization of Energy Management Based on Fuzzy Logic in an FCEV with a Contribution of a Photovoltaic Source
    (Springer, 2021) Belhad, Said; Ghedams, Kaci; Larabi, Zina
    In this article, we will study how to increase fuel economy, in a fuel cell/battery (FC + B) and fuel cell/battery/photovoltaic source (FC + B + PV) configuration in a hybrid vehicle with fuel cell. Hybrid vehicle models (FC + B) and (FC + B + PV) will be designed under Simulink/Matlab use a new hybrid approach based on the battery’s soc based on the availability of sunshine so the availability of the PV source. The results show that the proposed control of this strategy can meet the energy needs for good optimization in energy management. Comprehensive comparisons of this energy management strategy based on the control and monitoring of the fuel consumption of the fuel cell according to the chosen driving cycles. Therefore, the proposed strategy will provide a novel approach for advanced of energy management optimization system.