Communications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11
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Item Heuristic and learning method for obstacle avoidance with mobile robot(IEEE, 2020) Lachekhab, Fadhila; Acheli, Dalila; Tadjine, Mohamed; Meraihi, YassineIn 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 environmentItem PID Control of DC Servo Motor using a Single Memory Neuron(IEEE, 2018) Ladjouzi, Samir; Grouni, Said; Soufi, YoucefIn 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.Item 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, ZinaIn 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.Item 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 resultsItem Total organic carbon prediction in shale gas reservoirs using fuzzy logic(2015) Ouadfeul, Sid-Ali; Aliouane, LeilaItem 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, AmarThe 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 reservoirsItem A novel frequency control for a wind turbine generator associated with a flywheel(IEEE, 2017) Achour, D.; Kesraoui, M.; Chaib, A.Item Strategy of detections abnormal behavior by fuzzy logic(IEEE, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedItem Detection method without crowd behavior modeling by fuzzy logic(2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedItem Fuzzy logic based gradient descent method with application to a PI-type fuzzy controller tuning : new results(IEEE, 2007) Habbi, A.; Zelmat, M.