Article: Application of machine learning approach on improving quality of semi-trailer truck air suspensions Journal: International Journal of Vehicle Noise and Vibration (IJVNV) 2021 Vol.17 No.1/2 pp.101 - 120 Abstract: A combination of the machine-learning-approach (MLA) and optimal-fuzzy-logic-control (OFLC) is proposed for semi-trailer truck air suspensions to enhance the ride-quality and road-friendliness. A dynamics model of semi-trailer truck is built for the simulation. The root-mean-square accelerations of the vertical vibration (awzd), pitching (awφd), and rolling angle (awθd) of the tractor driver, and dynamic load-stress factor (κ) of wheel-axles are selected as indexes. Based on the data map of road surfaces and optimal rules of OFLC, the MLA is developed to control semi-trailer truck air suspensions. Results show that vehicle air suspensions optimised by OFLC and MLA are overall superior to passive air suspensions under different simulations. Especially, the awzd, awφd, awθd, and κ with MLA are markedly reduced by 13.29%, 11.05%, 17.78%, and 11.41% compared to OFLC under combined roads of level-E, level-C, and level-D. Consequently, the vehicle's ride-quality and road-friendly are further enhanced by applying MLA. Inderscience Publishers - linking academia, business and industry through research

Title: Application of machine learning approach on improving quality of semi-trailer truck air suspensions

Authors: Nguyen Van Liem; Yuan Huan

Addresses: School of Mechanical and Electric Engineering, Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi, 435003, China; Faculty of Automotive and Power Machinery Engineering, Thai Nguyen University of Technology, Thai Nguyen, 23000, Vietnam ORCID: https://orcid.org/0000-0001-8772-1086 ' School of Mechanical and Electric Engineering, Hubei Key Laboratory of Intelligent Conveying Technology and Device, Hubei Polytechnic University, Huangshi, 435003, China

Abstract: A combination of the machine-learning-approach (MLA) and optimal-fuzzy-logic-control (OFLC) is proposed for semi-trailer truck air suspensions to enhance the ride-quality and road-friendliness. A dynamics model of semi-trailer truck is built for the simulation. The root-mean-square accelerations of the vertical vibration (a_wzd), pitching (a_wφd), and rolling angle (a_wθd) of the tractor driver, and dynamic load-stress factor (κ) of wheel-axles are selected as indexes. Based on the data map of road surfaces and optimal rules of OFLC, the MLA is developed to control semi-trailer truck air suspensions. Results show that vehicle air suspensions optimised by OFLC and MLA are overall superior to passive air suspensions under different simulations. Especially, the a_wzd, a_wφd, a_wθd, and κ with MLA are markedly reduced by 13.29%, 11.05%, 17.78%, and 11.41% compared to OFLC under combined roads of level-E, level-C, and level-D. Consequently, the vehicle's ride-quality and road-friendly are further enhanced by applying MLA.

Keywords: semi-trailer trucks; ride quality; road-friendly; air suspensions; MLA; machine learning approach; genetic algorithm.

DOI: 10.1504/IJVNV.2021.120009

International Journal of Vehicle Noise and Vibration, 2021 Vol.17 No.1/2, pp.101 - 120

Received: 26 Oct 2020
Accepted: 23 Jun 2021
Published online: 04 Jan 2022 *

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Title: Application of machine learning approach on improving quality of semi-trailer truck air suspensions

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