A study on adaptive pavement unevenness estimation for road process noise considering under-spring information

ZOU Hantong1, 2, XIA Xiaojun1, ZHANG Hong1, ZHANG Zhifei2, CHEN Hao2, HE Yansong2

Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (14) : 283-292.

PDF(2938 KB)
PDF(2938 KB)
Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (14) : 283-292.
TRANSPORTATION SCIENCE

A study on adaptive pavement unevenness estimation for road process noise considering under-spring information

  • ZOU Hantong1,2,XIA Xiaojun1,ZHANG Hong1,ZHANG Zhifei*2,CHEN Hao2,HE Yansong2
Author information +
History +

Abstract

Accurately obtaining pavement unevenness information is crucial for intelligent suspension control, which directly affects vehicle dynamics and comfort. Therefore, this paper aims to improve the accuracy of pavement unevenness estimation, based on the 4-degree-of-freedom model, take the body vertical vibration, pitch vibration and unsprung vibration information as the observation quantities, use the Kalman filter algorithm to build the pavement unevenness estimation observer, meanwhile, construct the particle swarm-support vector machine model using the body vertical acceleration information to realize the pavement class classification, and based on the pavement class design, put forward an adaptive updating algorithm based on the process noise covariance matrix, taking into consideration the process noise information. Adaptive updating algorithm is designed based on pavement class, and the pavement process noise adaptive pavement unevenness estimation algorithm is proposed considering the unsprung information. The simulation results show that the proposed algorithm can improve the real-time pavement unevenness estimation accuracy compared with the conventional augmented Kalman filtering algorithm under random pavement and impact pavement. 

Key words

augmented Kalman observer / particle swarm optimization-support vector machines / pavement grade identification / process noise adaptation

Cite this article

Download Citations
ZOU Hantong1, 2, XIA Xiaojun1, ZHANG Hong1, ZHANG Zhifei2, CHEN Hao2, HE Yansong2. A study on adaptive pavement unevenness estimation for road process noise considering under-spring information[J]. Journal of Vibration and Shock, 2025, 44(14): 283-292

References

[1] 陈潇凯, 曾洺锴, 刘向, 等. 基于VSL-MPC的半主动悬架预瞄控制研究 [J]. 汽车工程, 2022, 44(10): 1537-46.
Chen Xiaokai, Zeng Mingkai, Liu Xiang, et al. Research on Semi-active Suspension Preview Control Based on VSL-MPC [J]. Journal of automobile engineering, 2022, 44(10): 1537-46.
[2] FAURIAT W, MATTRAND C, GAYTON N, et al. Estimation of road profile variability from measured vehicle responses [J]. Vehicle System Dynamics, 2016, 54(5): 585-605.
[3] LUSHNIKOV N, LUSHNIKOV P. Methods of Assessment of Accuracy of Road Surface Roughness Measurement with Profilometer [J]. Transportation Research Procedia, 2017, 20: 425-9.
[4] PREM H. A LASER-BASED HIGHWAY-SPEED ROAD PROFILE MEASURING SYSTEM [J]. Vehicle System Dynamics, 1988, 17(sup1): 300-4.
[5] NI T, LI W, ZHAO D, et al. Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle [J/OL] 2020, 20(13):10.3390/s20133676
[6] NGUYEN T, LECHNER B, WONG Y D. Response-based methods to measure road surface irregularity: a state-of-the-art review [J]. European Transport Research Review, 2019, 11(1): 43.
[7] QIN Y, DONG M, ZHAO F, et al. Road profile classification for vehicle semi-active suspension system based on Adaptive Neuro-Fuzzy Inference System; proceedings of the 2015 54th IEEE Conference on Decision and Control (CDC), F 15-18 Dec. 2015, 2015 [C].
[8] KIM G, LEE S Y, OH J S, et al. Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System [J]. IEEE Access, 2021, 9: 13878-90.
[9] 王雪玮, 李思渊, 梁晓, 等. 基于结构重参数化与自适应注意力的复杂路面快速识别模型 [J]. 中国公路学报: 1-18.
WANG Xue-wei, LI Si-yuan, LIANG Xiao, et al. Fast Identification Model for Complex Pavement based on Structural Reparameterization and Adaptive Attention. [J]. China Journal of Highway and Transport: 1-18.
[10] DOUMIATI M, ERHART S, MARTINEZ J, et al. Adaptive control scheme for road profile estimation: application to vehicle dynamics [J]. IFAC Proceedings Volumes, 2014, 47(3): 8445-50.
[11] TUDóN-MARTíNEZ J C, FERGANI S, SENAME O, et al. Adaptive Road Profile Estimation in Semiactive Car Suspensions [J]. IEEE Transactions on Control Systems Technology, 2015, 23(6): 2293-305.
[12] DOUMIATI M, MARTINEZ J, SENAME O, et al. Road profile estimation using an adaptive Youla–Kučera parametric observer: Comparison to real profilers [J]. Control Engineering Practice, 2017, 61: 270-8.
[13] YU W, ZHANG X, GUO K, et al. Adaptive Real-Time Estimation on Road Disturbances Properties Considering Load Variation via Vehicle Vertical Dynamics [J]. Mathematical Problems in Engineering, 2013, 2013: 283528.
[14] QIN Y, LANGARI R, WANG Z, et al. Road profile estimation for semi-active suspension using an adaptive Kalman filter and an adaptive super-twisting observer; proceedings of the 2017 American Control Conference (ACC), F 24-26 May 2017, 2017 [C].
[15] ZHAO B, NAGAYAMA T, XUE K. Road profile estimation, and its numerical and experimental validation, by smartphone measurement of the dynamic responses of an ordinary vehicle [J]. Journal of Sound and Vibration, 2019, 457: 92-117.
[16] 丁仁凯, 蒋俞, 汪若尘, 等. 考虑未知输入的主动悬架路面高程与等级识别研究 [J]. 汽车工程, 2021, 43(02): 278-86.
Ding Renkai, Jiang Yu, Wang Ruochen, et al. Research on Road Elevation and Grade Identification of Active SuspensionConsidering Unknown Inputs [J]. Journal of automobile engineering, 2021, 43(02): 278-86.
[17] 刘浪, 张志飞, 鲁红伟, 等. 基于增广卡尔曼滤波并考虑车辆加速度的路面不平度识别 [J]. 汽车工程, 2022, 44(02): 247-55+97.
Liu Lang, Zhang Zhifei, Lu Hongwei, Xu Zhongming. Based on augmented kalman filter and considering the vehicle acceleration of road roughness recognition [J]. Journal of automobile engineering, 2022, 44 (02) : 247-255 + 297.
[18] IMINE H, DELANNE Y, M'SIRDI N. Road profile input estimation in vehicle dynamics simulation [J]. Vehicle System Dynamics, 2006, 44(4): 285-303.
[19] 陈建华, 徐中明, 张志飞. 基于轴距预瞄的非匀速工况车辆悬架状态估计 [J]. 汽车工程, 2023, 45(06): 1040-9.
Chen Jianhua, Xu Zhongming, Zhang Zhifei. Suspension State Estimation Based on Wheelbase Preview at Variable Speed [J]. Journal of automobile engineering, 2023, 45(06): 1040-9.
[20] KWON B-S, KANG D, YI K. Wheelbase preview control of an active suspension with a disturbance-decoupled observer to improve vehicle ride comfort [J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2019, 234(6): 1725-45.
[21] WANG Z, DONG M, QIN Y, et al. Suspension system state estimation using adaptive Kalman filtering based on road classification [J]. Vehicle System Dynamics, 2017, 55(3): 371-98.
[22] 李韶华,李健玮,冯桂珍.基于GA-LSTM自适应卡尔曼滤波的路面不平度识别[J].振动与冲击,2024,43(09):121-130
Li ShaoHua, Li JianWei, Feng GuiZhen. Road roughness recognition based on GA-L STM adaptive K alman filtering [J]. Journal of Vibration and Shock, 2024,43(09):121-130
[23] 王亚,陈思忠,郑凯锋.时空相关路面不平度时域模型仿真研究[J].振动与冲击,2013,32(05):70-74.
Wang Ya, Chen SiZhong, Zheng KaiFeng. Simulation research on time domain model of road roughness with time-space correlation [J]. Journal of Vibration and Shock, 2013,32(05):70-74.
PDF(2938 KB)

84

Accesses

0

Citation

Detail

Sections
Recommended

/