Publications Scientifiques

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Start date: 2000Subject: 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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    Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions
    (JVE International, 2019) Gougam, Fawzi; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem
    Condition monitoring of rotating machines has become a more important strategy in structural health monitoring (SHM) research. For fault recognition, the analysis is categorized in two essential main parts: Feature extraction and classification; the first one is used for extracting the information from the signal and the other for decision-making based on these features. A higher accuracy is needed for sensitive places to avoid all kinds of damages that can lead to economic losses and it may affect the human safety as well. In this paper, we propose a new hybrid and automatic approach for bearing faults diagnosis. This method uses a combination between Empirical wavelet Transform (EWT) and Fuzzy logic System (FLS), in order to detect and localize the early degradation of bearing state under different working conditions. EWT build a wavelet filter bank to extract amplitude modulated-frequency modulated component of signal. Modes presenting a high impulsiveness is then selected using the kurtosis indicator. Thereafter, time domain features (TDFs) are applied for the reconstructed signal to extract the fault features which are finally used as an inputs of FLS in order to identify and classify the bearing states. The experimental results shows that the proposed method can accurately extract and classify the bearing fault under variable conditions. Moreover, performance of EWT and empirical mode decomposition (EMD) are studied and shows the superiority of the proposed method
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    PID Control of DC Servo Motor using a Single Memory Neuron
    (IEEE, 2018) Ladjouzi, Samir; Grouni, Said; Soufi, Youcef
    In this paper, a novel approach to determine the optimal values of a PID controller is presented. The proposed method is based on using a single memory neuron which its weights represent the PID parameters. These weights are updated by the well-known bio-inspired algorithm: the particle swarm optimization. To show the efficiency of our method, we have applied it to control a DC servo motor which is used as an actuator for an arm robot manipulator. The obtained results are compared with those a fuzzy logic controller.
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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.
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    Direct Torque Control of Three phase Induction Motor drive using Fuzzy Logic controllers for low Torque ripple
    (2013) Idir, A.; Kidouche, M.
    This paper presents an improved Direct Torque Control (DTC) based on fuzzy logic technique. The major problem that is usually associated with DTC drive is the high torque ripple. To overcome this problem a torque hysteresis band with variable amplitude is proposed based on fuzzy logic. The fuzzy proposed controller is shown to be able to reducing the torque and flux ripples and to improve performance DTC especially at low speed. The validity of the proposed methods is confirmed by the simulative results
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    Total organic carbon prediction in shale gas reservoirs using fuzzy logic
    (2015) Ouadfeul, Sid-Ali; Aliouane, Leila
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    Pore pressure prediction in shale gas reservoirs using neural network and fuzzy logic with an application to Barnett Shale
    (2015) Aliouane, Leila; Ouadfeul, Sid-Ali; Boudella, Amar
    The main goal of the proposed idea is to use the artificial intelligence such as the neural network and fuzzy logic to predict the pore pressure in shale gas reservoirs. Pore pressure is a very important parameter that will be used or estimation of effective stress. This last is used to resolve well-bore stability problems, failure plan identification from Mohr-Coulomb circle and sweet spots identification. Many models have been proposed to estimate the pore pressure from well-logs data; we can cite for example the equivalent depth model, the horizontal model for undercompaction called the Eaton’s model. . . etc. All these models require a continuous measurement of the slowness of the primary wave, some thing that is not easy during well-logs data acquisition in shale gas formtions. Here, we suggest the use the fuzzy logic and the multilayer perceptron neural network to predict the pore pressure in two horizontal wells drilled in the lower Barnett shale formation. The first horizontal well is used for the training of the fuzzy set and the multilayer perecptron, the input is the natural gamma ray, the neutron porosity, the slowness of the compression and shear wave, however the desired output is the estimated pore pressure using Eaton’s model. Data of another horizontal well are used for generalization. Obtained results clearly show the power of the fuzzy logic system than the multilayer perceptron neural network machine to predict the pore pressure in shale gas reservoirs