考虑转向滞后和车辆与路面状态的无人驾驶采矿卡车侧向控制

IF 0.6 Q4 TRANSPORTATION SCIENCE & TECHNOLOGY
Qiushi Chen, Guangqiang Wu, Qi Zeng, Jianzhuang Zong
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引用次数: 0

摘要

横向控制是无人驾驶采矿车系统的重要组成部分。然而,相当大的转向滞后和较差的跟踪精度限制了无人采矿的发展。本文设计了一个动态预览距离来抵消转向滞后。然后定义了车辆与目标道路之间的实时横向和航向误差的车辆-道路状态,以更有效地描述控制策略。为了权衡跟踪精度和稳定性,采用了高木-菅野(TS)模糊法来调整不同车辆-道路状态下线性二次调节器(LQR)的权重矩阵。基于实际矿山生产环境和 TR100 矿用卡车,实验结果表明 TS-LQR 算法的性能远远优于纯追求算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lateral Control for Driverless Mining Trucks with the Consideration of Steering Lag and Vehicle–Road States
Lateral control is an essential part of driverless mining truck systems. However, the considerable steering lag and poor tracking accuracy limit the development of unmanned mining. In this article, a dynamic preview distance was designed to resist the steering lag. Then the vehicle–road states, which described the real-time lateral and heading errors between the vehicle and the target road, was defined to describe the control strategy more efficiently. In order to trade off the tracking accuracy and stability, the Takagi–Sugeno (TS) fuzzy method was used to adjust the weight matrix of the linear quadratic regulator (LQR) for different vehicle–road states. Based on the actual mine production environment and the TR100 mining truck, experimental results show that the TS-LQR algorithm performed much better than the pure pursuit algorithm.
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来源期刊
SAE International Journal of Commercial Vehicles
SAE International Journal of Commercial Vehicles TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
1.80
自引率
0.00%
发文量
25
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