Yaw stability control strategy of modern trackless train

ZHANG Li-wei; LIU Jin-qi; ZHANG Meng-lei; SONG Zhong-chao

doi:10.19818/j.cnki.1671-1637.2023.02.017

Volume 23 Issue 2

Apr. 2023

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Article Navigation > Journal of Traffic and Transportation Engineering > 2023 > 23(2): 240-250

ZHANG Li-wei, LIU Jin-qi, ZHANG Meng-lei, SONG Zhong-chao. Yaw stability control strategy of modern trackless train[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 240-250. doi: 10.19818/j.cnki.1671-1637.2023.02.017

Citation:

ZHANG Li-wei, LIU Jin-qi, ZHANG Meng-lei, SONG Zhong-chao. Yaw stability control strategy of modern trackless train[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 240-250. doi: 10.19818/j.cnki.1671-1637.2023.02.017

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Yaw stability control strategy of modern trackless train

doi: 10.19818/j.cnki.1671-1637.2023.02.017

School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds:

Fundamental Research Funds for the Central Universities 2022YJS154

National Key Research and Development Program of China 2016YFB1200604-B22

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Author Bio:
ZHANG Li-wei(1977-), male, professor, PhD, 8134@bjtu.edu.cn.
Received Date: 2022-10-05
Available Online: 2023-05-09
Publish Date: 2023-04-25

Abstract

Abstract

To improve the problem of poor yaw stability and path tracking performance of modern trackless trains, a vehicle dynamics model was established based on the Lagrange equation, and the influence of hydraulic rod stiffness on the vehicle steering performance was analyzed. To solve the problem that the unknown constraints in the equation made it difficult to solve its quantitative relationship, the yaw rate error and trajectory tracking error were taken as optimization objectives, and the genetic algorithm was used for offline optimization of stiffness parameters. The function interpolation method was adopted for online prediction to obtain the optimal hydraulic rod stiffnesses under different vehicle speeds and front wheel angles. To improve the vehicle trajectory tracking performance, the yaw rate tracking error and trajectory tracking error were used as the criteria to evaluate the vehicle yaw stability. The lateral error and heading angle error of each axis in the vehicle driving process were defined. A vehicle yaw motion controller was designed based on the sliding mode control (SMC) algorithm, the expected yaw rate was calculated, and the stability proof and steady state error analysis were carried out. The proportional integral (PI) controller was used to calculate the yaw moment of the vehicle body distributed to each drive shaft, and simulation and test were conducted on the U-shaped curve path. Research results show that the hydraulic rod stiffness is directly related to the vehicle speed and the front wheel angle when the vehicle turns in a steady state, and in any case, the front hydraulic rod stiffness of the connection module must be greater than the rear hydraulic rod stiffness. When the vehicle speed is about 22 km·h^-1, the optimal hydraulic rod stiffness is the smallest. When the vehicle speed is greater than 22 km·h^-1, as the speed increases, the optimal hydraulic rod stiffness rises, and the change rate of the front hydraulic rod stiffness is significantly greater than that of the rear hydraulic rod stiffness. When the speed is less than 22 km·h^-1, as the speed gets smaller, the optimal hydraulic rod stiffness becomes greater. The lateral error of the axle on the straight section is less than 0.03 m, and the heading angle error is less than 0.03 rad. The lateral error of the axle on the curve section is less than 0.06 m, and the heading angle error is less than 0.06 rad. In the driving process, the yaw rate of the vehicle body can quickly track the given value, and the driving stability of the vehicle improves, which verifies the effectiveness of the control strategy.
- vehicle dynamics,
- modern trackless train,
- driving stability,
- genetic algorithm,
- sliding mode control

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References(32)

References

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Yaw stability control strategy of modern trackless train

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