Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

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End date: 2009Subject: search.filters.subject.Fuzzy logic

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    Fuzzy control motion design for mobile robots in unknown environments
    (2009) Hachour, O.
    we present an algorithm for path planning to a target for mobile robot in unknown environment. The proposed algorithm allows a mobile robot to navigate through static obstacles, and finding the path in order to reach the target without collision. This algorithm provides the robot the possibility to move from the initial position to the final position (target). The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In order to get an intelligent component, the use of Fuzzy Logic In order to get an intelligent component, the use of Fuzzy Logic (FL), and Expert Systems (ES) is necessary to bring the behavior of Intelligent Autonomous Vehicles (IAV). To present a real intelligent task and to deal with autonomy requirements such as power and thermal, (FL), and Expert Systems (ES) is necessary to bring the behavior of Intelligent Autonomous Vehicles (IAV). The aim work must make the robot able to achieve these tasks: to avoid obstacles, and to make ones way toward its target by ES-FL system capturing the behavior of a human expert. The integration of ES and FL has proven to be a way to develop useful realworld applications, and hybrid systems involving robust adaptive control. The proposed approach has the advantage of being generic and can be changed at the user demand. The results are satisfactory to see the great number of environments treated. The results are satisfactory and promising
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    Fuzzy logic controller for a pneumatic artificial muscle robot based on sliding mode control
    (2009) Rezoug, Abdellah; Meddahi, A.; Baizid, K.; Hamerlain, m.; Tadjine, M.
    Fuzzy Logic Control (FLC) has been successfully established in control systems engineering in the recent years, in other hand, Sliding Mode Control (SMC) is an active area in control research. The combination of this tow fields called Fuzzy Sliding Mode Control (FSMC) techniques to exploit the superior sides of these two controllers have drawn the attention of the scientific community. In this work, we proposed fuzzy logic controller based on the sliding mode theory to control the robot arm actuated by the pneumatics artificial muscles. Using bang-bang motion generation law, the objective of the control is the position and the velocity tracking by the robot. Simulations results demonstrate the feasibility and the advantages of our proposed research work.
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    Fuzzy clustering for finding fuzzy partitions of many-valued attribute domains in a concept analysis perspective
    (2009) Djouadi, Y.; Alouane, Basma; Prade, H.
    Although an overall knowledge discovery process consists of a distinct pre-processing stage followed by the data mining step, it seems that existing formal concept analysis (FCA) and association rules mining (ARM) approaches, dealing with many-valued contexts, mainly focus on the data mining stage. An "intelligent" pre-processing of input contexts is often absent in existing FCA/ARM approaches, leading to an unavoidable information loss. Usually, many-valued attribute domains need to be first fuzzily partitioned. However, it is unrealistic that the most appropriate fuzzy partitions can be provided by domain experts. In this paper, an unsupervised learning stage, based on Fuzzy C-Means algorithm, is proposed in order to get fuzzy partitions that are faithful to data for quantitative attribute domains, and consequently for avoiding the loss of valuable association rules due to the use of empirical fuzzy partitions. More precisely, the paper reports an experiment where it is shown that some rules are no longer found because their support or confidence is too low when using such empirical partitions. Experimental results show that the learned fuzzy partition outperforms human expert fuzzy partitions. More generally, the paper provide discussions about the handling of many-valued attributes in both fuzzy FCA and fuzzy ARM