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Active neuro-fuzzy integrated vehicle dynamics controller to improve the vehicle handling adn stability at complicated maneuvers

机译:主动神经模糊综合车辆动力学控制器,在复杂机动中提高车辆操纵稳定性

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摘要

With the recent advancements in vehicle’s industry, driving safety inpassenger vehicles is considered one of the key issues in designing any vehicle.According to other studies Electronic Stability Control (ESC) is consideredto be the greatest road safety innovation since the seatbelt. Yet ESC hasits drawbacks, that encouraged the development of other stability systems tocorrect or compensate these draw backs. But to efficiently make up for theESC problems the integration of various control systems is needed, which isa pretty complicated task on its own. Lately, solving this stability problembecame a hot research topic accompanied by the market demands for improvingthe available stability systems.Therefore, this thesis aims to add an innovative approach to help improvethe vehicle stability. This approach consists of an intelligent algorithm thatcollects data about the vehicle characteristics and behavior. Then it uses anArtificial Neural Network to construct a fuzzy logic control system throughlearning from the optimum control values that was generated beforehand bythe intelligent algorithm. This way, the proposed controller didn’t depend onlyon experts’ knowledge like the other controllers presented in the literature.This makes the controller more generic and reliable which is a very importantaspect in designing a safety critical controller, like the presented one, whereany fault in it can lead to a fatal accident.Also using the technique of using an Artificial Neural Network to constructa fuzzy logic control allows benefiting from the learning and autoautoadaptioncapability of neural networks and the smooth controlling performancethat fuzzy logic controllers offers.Simulations results show the effectiveness of the proposed controller forimproving the vehicle stability in different driving maneuvers. Where the controller’sresults were compared to an uncontrolled vehicle and another vehicle controlled by a controller from the literature. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
机译:随着汽车工业的最新发展,驾驶安全性无人驾驶车辆被认为是设计任何车辆的关键问题之一。根据其他研究,电子稳定控制(ESC)被认为是自安全带以来最大的道路安全创新。然而,ESC有其缺点,它鼓励开发其他稳定性系统来纠正或补偿这些缺点。但是,要有效地解决ESC问题,需要集成各种控制系统,这本身就是一项非常复杂的任务。近年来,解决这一稳定性问题成为研究的热点,伴随着市场对改进现有稳定性系统的需求。因此,本论文旨在增加一种创新的方法来帮助提高车辆的稳定性。这种方法由智能算法组成,该算法收集有关车辆特性和行为的数据。然后利用人工神经网络,通过学习智能算法预先生成的最优控制值,构造出模糊逻辑控制系统。这样一来,拟议的控制器就不再像文献中介绍的其他控制器那样仅依赖于专家的知识,这使得该控制器更加通用和可靠,这在设计安全关键型控制器(如所提出的控制器)时非常重要。通过使用人工神经网络技术构建模糊逻辑控制技术,还可以受益于神经网络的学习和自动适应能力以及模糊逻辑控制器提供的平滑控制性能。仿真结果证明了该算法的有效性。提出的控制器可以改善不同驾驶操作中的车辆稳定性。将管制员的结果与不受管制的车辆和由文献中的管制员控制的另一辆车进行比较。 -------------------------------------------------- -------------------------------------------------- -------------------------------------------------- -------------------------------------------------

著录项

  • 作者

    Raouf Hasan-Farag Rana;

  • 作者单位
  • 年度 2013
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  • 原文格式 PDF
  • 正文语种 eng
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