中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2019, Vol. 38 ›› Issue (08): 1-6.DOI: 10.3969/j.issn.1674-0696.2019.08.01

• 交通+大数据人工智能 •    下一篇

自动驾驶汽车的轨迹跟踪控制

邵毅明1,陈亚伟2,束海波2   

  1. (1. 重庆交通大学 交通运输学院,重庆 400074; 2. 重庆交通大学 机电与车辆工程学院,重庆 400074)
  • 收稿日期:2018-01-15 修回日期:2018-10-28 出版日期:2019-08-01 发布日期:2019-08-01
  • 作者简介:邵毅明(1955—),男,四川资阳人,教授,博士生导师,主要从事道路车辆交通安全方面的研究。Email:2786343568@qq.com。 通信作者:陈亚伟(1993—),男,甘肃定西人,硕士研究生,主要从事车辆系统动力学及控制方面的研究。Email:414547625@qq.com。
  • 基金资助:
    重庆市重点产业共性关键技术创新专项资助项目(cstc2015zdcy-ztzx30001)

Trajectory Tracking Control of Self-Driving Cars

SHAO Yiming1, CHEN Yawei2, SHU Haibo2   

  1. (1. School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, P. R. China; 2. School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, P. R. China)
  • Received:2018-01-15 Revised:2018-10-28 Online:2019-08-01 Published:2019-08-01

摘要: 通过建立车辆四自由度动力学模型,基于轮胎魔术公式考虑侧偏角软约束,应用模型预测控制理论设计了车辆轨迹跟踪控制器。该控制器在保证车辆操纵稳定性的同时,能通过控制前轮转角使自动驾驶汽车自主完成轨迹跟踪控制。仿真结果表明:与最优预瞄控制算法相比,带有侧偏角软约束的线性时变模型预测控制器在轨迹跟踪方面有更好的优越性,对车速变化和参考轨迹具有良好的适应性,可提高自动驾驶汽车的轨迹跟踪能力。

关键词: 车辆工程, 自动驾驶, 模型预测, 轨迹跟踪

Abstract: A four-degree-of-freedom dynamic model was established. Based on the tire magic formula, considering soft constraints of sideslip angle, the vehicle trajectory tracking controller was designed by applying model prediction control theory. The controller could ensure the vehicle handling stability; meanwhile, it could make the self-driving vehicle complete the trajectory tracking control autonomously by controlling the front wheel angle. The simulation results show that compared with the optimal preview control algorithm, the proposed linear time-varying model prediction controller considering soft constraints of sideslip angle has better superiority in trajectory tracking, has good adaptability to vehicle speed variation and reference trajectory, and can improve the trajectory tracking ability of the autopilot vehicle.

Key words: vehicle engineering, autopilot, model prediction, trajectory tracking

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