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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(2): 205-218
LI Jun, TANG Shuang, HUANG Zhi-xiang, ZHOU Wei. Longitudinal and lateral coordination control method of high-speed unmanned vehicles with integrated stability[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 205-218. doi: 10.19818/j.cnki.1671-1637.2020.02.017
Citation: LI Jun, TANG Shuang, HUANG Zhi-xiang, ZHOU Wei. Longitudinal and lateral coordination control method of high-speed unmanned vehicles with integrated stability[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 205-218. doi: 10.19818/j.cnki.1671-1637.2020.02.017
shu

Longitudinal and lateral coordination control method of high-speed unmanned vehicles with integrated stability

doi: 10.19818/j.cnki.1671-1637.2020.02.017
  • 1.

    College of Mechanical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China

  • 2.

    Chongqing Key Laboratory of Rail Vehicle System Integration and Control, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Natural Science Foundation of China 51705051

Natural Science Foundation of Chongqing cstc2018jcyjAX0422

More Information
  • Author Bio:

    LI Jun(1964-), male, professor, PhD, E-mail: cqleejun@163.com

  • Corresponding author: LI Jun(1964-), male, professor, PhD, E-mail: cqleejun@163.com
  • Received Date: 2019-11-14
  • Publish Date: 2020-04-25
    Abstract
  • A path tracking control method considering longitudinal and lateral coordination control was proposed. The vehicle preview error model and high-speed vehicle equivalent dynamics model considering road surface terrain were established to introduce road curvature terrain factors. The preview distance generator based on the fuzzy rules was designed to solve the problem of fixed preview distance in the preview error model. The function relationship between the time domain and the road curvature was established. The model predictive control algorithm was used to solve the front wheel rotation angle, thereby establishing a path tracking controller. The expected vehicle speed was represented by the exponential model, and the proportion integration differentiation longitudinal controller was designed to improve the path tracking accuracy. The vehicle stability characteristic was represented by phase plane of slip angle, and the proportion integration differentiation stability controller was designed to improve the vehicle stability. Research result shows that the control method can optimize the vehicle tracking performance on the roads with different adhesion coefficients. When driving on a dry asphalt pavement at a speed of 90 km·h-1, the maximum lateral error reduces by 33% compared with a vehicle that only uses model predictive control algorithm for path tracking control. When driving on a wet asphalt pavement at a speed of 70 km·h-1, the maximum lateral error reduces by 30% compared with a vehicle that only uses model predictive control algorithm for path tracking control. When driving on an icy and snow pavement at a speed of 55 km·h-1, the maximum lateral error reduces by 16% compared with a vehicle that only uses model predictive control algorithm for path tracking control. Therefore, the proposed control method can effectively improve the path tracking accuracy.

     

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