Publications Internationales
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Item Modern artificial intelligence technics for unmanned aerial vehicles path planning and control(2025) Zamoum, Yasmine; Baiche, Karim; Benkeddad, Youcef; Bouzida, BrahimUnmanned 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.Item 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, NaoualThe 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 changeItem 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, BoualemCondition 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 methodItem Decentralized robust sliding mode control for a class of interconnected nonlinear systems with strong interconnections(2017) Deia, Yacine; Kidouche, Madjid; Becherif, MohamedItem Intelligent detection without modeling of behavior unusual by fuzzy logic(Springer, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedItem Fuzzy rules for joint integration of production schedule and maintenance planning(Old City Publishing, 2016) Berrichi, Ali; Yalaoui, Farouk; Yalaoui, AliceItem Impulse noise reduction in 2D electrical resistivity imaging data based on fuzzy logic(IEEE, 2011) Ferahtia, J.; Djarfour, Noureddine; Baddari, Kamel; Khaldoun, AsmaeItem Magnetic susceptibility and its relation with fractures and petrophysical parameters in the tight sand oil reservoir of Hamra quartzites, southwest of the Hassi Messaoud oil field, Algeria(Elsevier, 2014) Aïfa, Tahar; Ali Zerrouki, Ahmed; Baddari, Kamel; Géraud, YvesItem Fuzzy logic-based controller for position regulation of electric drives(Advances in Modelling and Analysis C, 2007) Chermalikh, A.V.; Chetate, Boukhmis; Maidanski, I.I.; Kheldoum, A.Electrical drives are characterized by their natural non- linearity owing to their proper design and their time-varying mathematical models. When used to drive industrial systems, e.g. variable speed or variable position drives, conventional control methods are usually applied to design speed and position controllers. However, at certain performance level, these methods are not satisfied. The present paper combines fuzzy logic, mostly used to control system characterized by non-linearity and uncertainty, with new control structures to overcome difficulties listed earlier. The obtained results have proved the good foundation of the suggested methodItem Fuzzy approach applied in fault detection and lsolation to the compression system control(2010) Hafaifa, A.; Laaouad, F.; Laroussi, K.