计算机科学
控制器(灌溉)
控制理论(社会学)
航向(导航)
弹道
非完整系统
理论(学习稳定性)
路径(计算)
偏航
航程(航空)
欧拉角
控制(管理)
人工智能
机器人
移动机器人
数学
工程类
航空航天工程
汽车工程
程序设计语言
天文
物理
机器学习
生物
农学
几何学
作者
Ahmed M. Abdelmoniem,Ahmed Osama,Mohamed Abdelaziz,Shady A. Maged
标识
DOI:10.1177/1729881420974852
摘要
Path tracking is one of the most important aspects of autonomous vehicles. The current research focuses on designing path-tracking controllers taking into account the stability of the yaw and the nonholonomic constraints of the vehicle. In most cases, the lateral controller design relies on identifying a path reference point, the one with the shortest distance to the vehicle giving the current state of the vehicle. That restricts the controller’s ability to handle sudden changes of the trajectory heading angle. The present article proposes a new approach that imitates human behavior while driving. It is based on a discrete prediction model that anticipates the future states of the vehicle, allowing the use of the control algorithm in future predicted states augmented with the current controller output. The performance of the proposed approach is verified through several simulations on V-REP simulator with different types of maneuvers (double lane change, hook road, S road, and curved road) and a wide range of velocities. Predictive Stanley controller was used compared to the original Stanley controller. The obtained results of the proposed control approach show the advantage and the performance of the technique in terms of minimizing the lateral error and ensuring yaw stability by an average of 53% and 22%, respectively.
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