[1] Xu Shaoyong, Zhang Jianrun, Nguyen Van Liem, Applying machine learning for cars’ semi-activeair suspension under soft and rigid roads [J]. Journal of Southeast University (English Edition), 2022, 38 (3): 300-308. [doi:10.3969/j.issn.1003-7985.2022.03.012]

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Applying machine learning for cars’ semi-activeair suspension under soft and rigid roads()

汽车半主动空气悬架在软与硬路面下机器学习中的应用

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

38

Issue:

2022 3

Page:

300-308

Research Field:

Traffic and Transportation Engineering

Publishing date:

2022-09-20

Info

Title:

Applying machine learning for cars’ semi-activeair suspension under soft and rigid roads

汽车半主动空气悬架在软与硬路面下机器学习中的应用

Author(s):

Xu Shaoyong¹;  Zhang Jianrun²;  Nguyen Van Liem¹;  2

¹ School of Mechanical and Electrical Engineering, Hubei Polytechnic University, Huangshi 435003, China
¹ Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi 435003, China
² School of Mechanical Engineering, Southeast University, Nanjing 211189, China

徐绍勇¹;  张建润²;  阮文廉¹;  2

¹ 湖北理工学院机电工程学院, 黄石 435003; ¹ 湖北理工学院智能输送技术与装备湖北重点实验室, 黄石 435003; ² 东南大学机械工程学院, 南京 211189

Keywords:

semi-active air suspension;  ride quality;  machine learning;  fuzzy control;  genetic algorithm

半主动空气悬架;  平顺性;  机器学习;  模糊控制;  遗传算法

PACS:

U461.3

DOI:

10.3969/j.issn.1003-7985.2022.03.012

Abstract:

To improve the ride quality and enhance the control efficiency of cars’ semi-active air suspensions(SASs)under various surfaces of soft and rigid roads, a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs. The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions. The results indicate that a soft surface obviously influences a car’s ride quality, particularly when it is traveling at a high-velocity range of over 72 km/h. Using the ML method, the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions. In particular, compared with those cars without control, the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road. In addition, to optimize the ML efficiency, its learning data need to be updated under all various operating conditions of cars.

为提高车辆半主动空气悬架在不同路面下的平顺性和控制性能, 以驾驶员座椅和车辆俯仰角的加权加速度均方根值为控制目标, 提出了一种基于模糊最优控制和车辆实际模型的机器学习方法.研究结果表明:车辆在72 km/h以上高速行驶时, 软路面对车辆的平顺性有明显影响.基于机器学习, 软路面工况下采用模糊控制的座椅和车辆俯仰角的加权加速度均方根值分别降低了30.20% 和19.95%, 而硬路面工况下无控制策略的座椅加速度和俯仰角加速度的加权加速度均方根值分别降低了34.36%和21.66%.这说明不同仿真条件下, 该方法均能提高车辆的行驶平顺性.此外, 为提高机器学习的效率, 需要对其学习数据进行不断更新, 以适应车辆的各种运行工况.

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Memo

Memo:

Biographies: Xu Shaoyong(1981—), male, doctor; Zhang Jianrun(corresponding author), male, doctor, professor, zhangjr@seu.edu.cn.
Foundation items: The National Key Research and Development Plan(No. 2019YFB2006402), Talent Introduction Fund Project of Hubei Polytechnic University(No. 17xjz01R), Key Scientific Research Project of Hubei Polytechnic University(No. 22xjz02A).
Citation: Xu Shaoyong, Zhang Jianrun, Nguyen Van Liem. Applying machine learning for cars’ semi-active air suspension under soft and rigid roads[J].Journal of Southeast University(English Edition), 2022, 38(3):300-308.DOI:10.3969/j.issn.1003-7985.2022.03.012.

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