Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (12): 3793-3803.doi: 10.13229/j.cnki.jdxbgxb.20240800

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Semi⁃trailer stability parameter estimation based on genetic algorithm and recursive least squares method

Xiao-hua ZENG1(),Kai-xuan LI1,Kai HAN2,Ming-yao GONG3(),Yu-feng HUANG1   

  1. 1.National Key Laboratory of Automotive Chassis Integration and Bionics,Jilin University,Changchun 130022,China
    2.The State Key Laboratory of Engine and Powertrain System,Weichai Power Co. ,Ltd. ,Weifang,262123,China
    3.School of Aeronautical Engineering,Jilin University of Chemical Technology,Jilin 132102,China
  • Received:2024-05-14 Online:2025-12-01 Published:2026-02-03
  • Contact: Ming-yao GONG E-mail:zeng.xiaohua@126.com;gongmingyuan@jlict.edu.cn

Abstract:

A combination of genetic algorithm and recursive least squares method was used to estimate the stability parameters of a semi-trailer, solving the problem that parameters such as tire cornering stiffness, vehicle roll stiffness and body roll damping are difficult to directly measure through sensors. In terms of adaptability to working conditions, the shortcomings of traditional offline identification methods are effectively made up for via this method. Compared with general commercial vehicles, semi-trailers have a more complex structure and diversified operating conditions. Therefore, in the process of ensuring vehicle safety and quality, greater attention must be paid to the stability control of semi-trailers. The prerequisite for achieving this goal is to establish a high-precision and high-confidence theoretical model of semi-trailer dynamics. On this basis,the theoretical model can be used as a following target, and the difference between the output state of the actual vehicle or commercial software vehicle model and the output state of the theoretical model is used as the control variable for adjustment. Joint simulation of Trucksim and Simulink is used in this paper to compare the output overlap between the Trucksim software model and the theoretical model under specific input and working conditions. Results show that the theoretical model established based on the parameters such as roll stiffness indentifled by the method proposed in this paper is superior to traditional offline identification method in terms of operating condition adaptability and accuracy, the estimation error is reduced by about 6%. This result lays the foundation for subsequent semi-traller stability control research based on this theoretical model.

Key words: vehicle engineering, semi-trailer stability, parameter estimation, genetic algorithm, recursive least squares method

CLC Number: 

  • U461.6

Fig.1

Semi-trailer car train vehicle coordinate system"

Fig.2

Diagram of the yaw and lateral motion of the semi-trailer"

Fig.3

Semi-trailer roll motion diagram"

Table 1

Main structural parameters of the semi-trailer vehicle model"

参数名称符号数值单位
牵引车总质量m16 762kg
牵引车簧载质量m1s5 457kg
挂车总质量m216 665kg
挂车簧载质量m2s16 000kg
牵引车质心到前轴距离d11.113m
牵引车质心到后轴距离d22.387m
挂车质心到铰接点距离d35.221m
挂车质心到挂车轴距离d44.779m
牵引车质心到铰接点距离d52.287m
牵引车质心高度h11.173m
挂车质心高度h21.935m
铰接点距地面距离hc1.110m
牵引车绕x轴转动惯量I1xx2 287kg?m2
挂车绕x轴转动惯量I2xx25 350kg?m2
牵引车绕z轴转动惯量I1zz34 823kg?m2
挂车绕z轴转动惯量I2zz135 000kg?m2
牵引车绕xz轴惯量积I1xz1 626kg?m2
挂车绕xz轴惯量积I2xz0kg?m2

Fig.4

Flow chat of semi-trailer stability parameters"

Fig.5

Relationship between the objective function and number of generations"

Table 2

Estimation results f stability parameters of semi-trailer o"

稳定性参数项估计值
牵引车前轴轮胎等效侧偏刚度k1/(N·rad-1-2.96×105
牵引车后轴轮胎等效侧偏刚度k2/(N·rad-1-5.74×105
挂车轴轮胎等效侧偏刚度k3/(N·rad-1-6.51×105
牵引车车身侧倾刚度kr1/(N·m·rad-12.07×104
牵引车车身侧倾阻尼c1/(N·m·s·rad-13.62×105
挂车车身侧倾刚度kr2/(N·m·rad-16.05×105
挂车车身侧倾阻尼c2/(N·m·s·rad-13.49×105
铰接点等效侧倾刚度k12/(N·m·rad-11.53×107

Fig.6

40 km/h sinusoidal input the vehicle status under identifies working conditions"

Fig.7

Centroid side declination angle of tractor and semi-trailer under different test conditions"

Fig.8

Estimation results of stability parameters based on fusion genetic algorithm and RLS"

Fig.9

Combining genetics algorithm and RLS to estimate yaw velocity of a tractor and semi-trailer under different test conditions"

Table 3

Mean value of the residual inder MSE Calculated from state variable under two identification methods"

辨识方法MSE均值
单一遗传算法离线辨识0.196
带遗忘因子RLS融合辨识0.077

Fig.10

Calculated residual MSE of state variable under two identification methods"

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