基于数据模糊的ALV横向控制器设计与实验研究

M. Fu, X. Feng, Meiling Wang, Jiuhong Ruan
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引用次数: 1

摘要

自主陆地车辆是一个复杂的、非线性的、不确定的系统。目前,ALV研究领域的一个关键问题是运动控制缺乏鲁棒性。为了解决这一问题,本文首先建立了自动驾驶汽车的运动学模型。然后,基于“数据-模糊”模糊逻辑系统,设计了自动驾驶汽车Mamdani推理系统的横向控制器。控制器的设计不仅利用了输入-输出数据信息,而且利用了专家生成的语言信息,因此设计的控制器具有更好的性能,能够适应大范围的速度和不同的道路条件。实验结果表明,所设计的控制器具有足够的鲁棒性,能够使ALV快速准确地跟随参考路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on data-fuzzy-based ALV lateral controller design and experiments
Autonomous land vehicle is a complex, nonlinear and uncertain system. At present, one of the key problems in the researching field of ALV is the lack of robustness in motion control. In order to solve this problem, the kinematics model of ALV was presented at first in this paper. Then based on fuzzy logical system of "data-fuzzy", a lateral controller of Mamdani reasoning system for ALV was designed. The design of the controller utilized not only the in-out data information but also the language information generated by expertise, so the controller designed have a better performance, and can accommodate a wide range of velocity and different conditions of the road. The experiment results show that the controller designed has enough robustness, and can make ALV follow reference paths quickly and accurately.
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