汽车工程 ›› 2022, Vol. 44 ›› Issue (1): 17-25.doi: 10.19562/j.chinasae.qcgc.2022.01.003

所属专题: 智能网联汽车技术专题-规划&控制2022年

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基于模糊LQR的智能汽车路径跟踪控制

胡杰(),钟鑫凯,陈瑞楠,朱令磊,徐文才,张敏超   

  1. 1.武汉理工大学,现代汽车零部件技术湖北省重点实验室,武汉  430070
    2.武汉理工大学,汽车零部件技术湖北省协同创新中心,武汉  430070
    3.新能源与智能网联车湖北工程技术研究中心,武汉  430070
  • 收稿日期:2021-10-09 修回日期:2021-10-25 出版日期:2022-01-25 发布日期:2022-01-21
  • 通讯作者: 胡杰 E-mail:auto_hj@163.com
  • 基金资助:
    湖北省技术创新专项(2019AEA169);湖北省科技重大专项(2020AAA001)

Path Tracking Control of Intelligent Vehicles Based on Fuzzy LQR

Jie Hu(),Xinkai Zhong,Ruinan Chen,Linglei Zhu,Wencai Xu,Minchao Zhang   

  1. 1.Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan  430070
    2.Wuhan University of Technology,Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan  430070
    3.Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan  430070
  • Received:2021-10-09 Revised:2021-10-25 Online:2022-01-25 Published:2022-01-21
  • Contact: Jie Hu E-mail:auto_hj@163.com

摘要:

为保证智能汽车在不同车速下路径跟踪的精确性与稳定性,本文中设计了一种带有预瞄PID转角补偿的模糊线性二次型调节器(LQR)以进行路径跟踪控制。首先,基于路径跟踪误差模型设计了LQR控制器,并采用预瞄PID方法进行转角补偿,消除稳态误差,提高跟踪精度。接着,针对固定权重系数的控制器对于不同车速适应性较差的问题,提出了一种基于车速的权重系数模糊调节策略。最后,通过实车试验,验证了控制器在实车环境中的控制性能。结果表明,设计的控制器具有较高的跟踪精度,且在不同车速下均能保持良好的精确性与稳定性。

关键词: 智能汽车, 路径跟踪, 预瞄PID, 模糊LQR

Abstract:

In order to ensure the accuracy and stability of the path tracking of intelligent vehicles under different speeds, a fuzzy linear quadratic regulator (LQR) with rotation angle compensation using preview PID for path tracking control is designed in this paper. Firstly, the LQR controller is designed based on the path tracking error model, and the preview PID algorithm is used to compensate the rotation angle,eliminate the steady-state error and enhance the tracking accuracy. Then, aiming at the problem of poor adaptability to different speeds of the controller with fixed weighting factors, a speed-based fuzzy adjustment strategy of weighting factors is proposed. Finally, a real vehicle test is conducted to verify the control performance of the controller in real world environment. The results show that the controller designed has high tracking accuracy, and can maintain good accuracy and stability under different vehicle speeds.

Key words: intelligent vehicles, path tracking, preview PID, fuzzy LQR