[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()

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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

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

Keywords:

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

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.

References:

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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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