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汽车安全与节能学报 ›› 2020, Vol. 11 ›› Issue (4): 462-469.DOI: 10.3969/j.issn.1674-8484.2020.04.005

• 汽车安全 • 上一篇    下一篇

基于最优预瞄和模型预测的智能商用车路径跟踪控制

李耀华(), 刘洋, 冯乾隆, 南友飞, 何杰, 范吉康   

  1. 长安大学汽车学院,西安710064,中国
  • 收稿日期:2020-07-18 出版日期:2020-12-30 发布日期:2021-01-04
  • 作者简介:李耀华(1980–),男(汉),陕西,副教授。E-mail:nuaaliyaohua@126.com
  • 基金资助:
    国家自然科学基金项目(51207012);陕西省工业科技攻关项目(2016GY-069);陕西省自然科学基金项目(2020JQ-385);陕西省“微特电机及驱动技术”重点实验室开放基金项目(2013SSJ2002);中央高校基本科研业务费专项资金资助项目(300102228201)

Path tracking control for an intelligent commercial vehicle based on optimal preview and model predictive

LI Yaohua(), LIU Yang, FENG Qianlong, NAN Youfei, HE Jie, FAN Jikang   

  1. School of Automobile, Chang’an University, Xi’an 710064, China
  • Received:2020-07-18 Online:2020-12-30 Published:2021-01-04

摘要:

为解决智能商用车路径跟踪问题,采用一种最优预瞄控制策略。根据商用车航向角与路径曲率的关系,引入航向角偏差反馈控制;根据车速与预瞄距离的关系,提出了变权重因数的多点预瞄距离确定方法。为了保证商用车路径跟踪的稳定性,采用模型预测控制策略,对车轮侧偏角进行约束。通过TruckSim与Simulink联合仿真,对比分析了侧向偏差、横摆角速度和前轮侧偏角变化情况。结果表明:最优预瞄控制策略对车速变化具有较好的适应性,但当路面附着因数较低时,车辆会失去稳定性;模型预测控制策略对车速和路面附着因数变化都具有较好的适应性,行驶稳定性更好,且比最优预瞄控制策略具有更精确的路径跟踪效果。

关键词: 智能商用车, 路径跟踪, 路面附着因数, 最优预瞄控制, 模型预测控制

Abstract:

An optimal preview control strategy was adopted to solved path tracking problem of intelligent commercial vehicles. According to the relationship between the heading angle and the curvature of the path, the heading angle deviation feedback control was introduced. According to the relationship between the speed and the preview distance, a multi-point preview distance determination method with variable weight coefficient was proposed. In order to ensure the stability of path tracking, the model predictive control was used to restrict the wheel sideslip angle. Through co-simulation of TruckSim and Simulink, the lateral deviations, the yaw rates and the front wheel slip angles were compared. The results show that the optimal preview control has good adaptability to the speed, but when the road adhesion factor is low, the vehicle will lose stability; The model predictive control has better adaptability to speeds and road adhesion factors, and has better driving stability, and has more accurate path tracking effect than the optimal preview control.

Key words: intelligent commercial vehicles, path tracking, road adhesion factors, optimal preview control, model predictive control

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