Volume 25 Issue 3
Jun.  2025
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2025  >  25(3): 269-283
Next Previous
WU Xiao, CHEN Zhi-yong, CHEN Long, LIU Qiao-bin, YANG Hong-bo, SHI Wen-ku. Research on suspension model switching preview control based on road surface vertical excitation game decision[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 269-283. doi: 10.19818/j.cnki.1671-1637.2025.03.018
Citation: WU Xiao, CHEN Zhi-yong, CHEN Long, LIU Qiao-bin, YANG Hong-bo, SHI Wen-ku. Research on suspension model switching preview control based on road surface vertical excitation game decision[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 269-283. doi: 10.19818/j.cnki.1671-1637.2025.03.018
shu

Research on suspension model switching preview control based on road surface vertical excitation game decision

doi: 10.19818/j.cnki.1671-1637.2025.03.018
  • 1.

    College of Automotive Engineering, Jilin University, Changchun 130022, Jilin, China

  • 2.

    College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China

  • 3.

    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, Guangdong, China

  • 4.

    School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China

Funds:

Joint Fund of National Natural Science Foundation of China U24A6008

China Postdoctoral Science Foundation Project 2024M753542

Open Project of the State Key Laboratory of Automotive Simulation and Control 20230101

More Information
  • Corresponding author: CHEN Zhi-yong (1980-), male, professor, PhD, chen_zy@jlu.edu.cn
  • Received Date: 2024-08-26
  • Accepted Date: 2025-04-30
  • Rev Recd Date: 2025-02-14
  • Publish Date: 2025-06-28
    Abstract
  • To address the inconsistency between apparent road surface roughness and actual vertical road excitation, a game theory-based vertical road excitation perception and decision-making method was proposed for preview control of the suspension model, aiming to improve the ride comfort of vehicles under complex road excitation conditions. Based on optimal suspension control theory, a multi-objective optimization algorithm was used to optimize the control models for different road excitation patterns. By integrating the control models under all excitation patterns, a suspension model switching control system was established. The controller parameters were switched according to vertical road excitation so that the vibration state of the suspension was kept optimal. The vibration responses under the same road excitation and different controller parameters were compared, and the influence of controller parameters on vibration responses was analyzed. Suspension control was regarded as a game between the controller and the road excitation. To improve ride comfort, when the results of the road preview method and the state observation method were contradictory under different road excitations, the optimal result was analyzed based on game theory and used as the basis for suspension control model switching. Analysis results show that based on the game theory, the control system should switch the control model according to the power spectral density index or road amplitude to improve ride comfort. Compared with the pure preview control model without game theory, on continuous road surfaces, when the preview result is grade A, and the state observation result is grade D, the root mean square (RMS) of vehicle acceleration of the optimal control model based on the game theory decreases by 14.24%. On the impact road surfaces, the peak value of the vehicle acceleration decreases by 5.86%. On mixed road surfaces, the RMS of the vehicle acceleration decreases by 11.60%, and the RMS of suspension's dynamic deflection, tire's dynamic travel, and energy consumption all increase by less than 10%. This method is applicable to improving the ride comfort of vehicles equipped with preview suspension systems under various complex road conditions.

     

    • FullText(HTML)
  • loading
    • References(32)
  • [1]
    THEUNISSEN J, TOTA A, GRUBER P, et al. Preview-based techniques for vehicle suspension control: a state-of-the-art review[J]. Annual Reviews in Control, 2021, 51: 206-235.
    [2]
    ZHAO D X, WANG L L, LI Y L, et al. Extraction of preview elevation of road based on 3D sensor[J]. Measurement, 2018, 127: 104-114.
    [3]
    CHEN Xiao-kai, ZENG Ming-kai, LIU Xiang, et al. Research on semi-active suspension preview control based on VSL-MPC[J]. Automotive Engineering, 2022, 44(10): 1537-1546.
    [4]
    GOHRLE C, SCHINDLER A, WAGNER A, et al. Road profile estimation and preview control for low-bandwidth active suspension systems[J]. IEEE/ASME Transactions on Mechatronics, 2014, 20(5): 2299-2310.
    [5]
    GOHRLE C, SCHINDLER A, WAGNER A, et al. Design and vehicle implementation of preview active suspension controllers[J]. IEEE Transactions on Control Systems Technology, 2014, 22(3): 1135-1142.
    [6]
    WU J, ZHOU H L, LIU Z Y, et al. Ride comfort optimization via speed planning and preview semi-active suspension control for autonomous vehicles on uneven roads[J]. IEEE Transactions on Vehicular Technology, 2020, 69(8): 8343-8355.
    [7]
    THEUNISSEN J, SORNIOTTI A, GRUBER P, et al. Regionless explicit model predictive control of active suspension systems with preview[J]. IEEE Transactions on Industrial Electronics, 2020, 67(6): 4877-4888.
    [8]
    DU Y C, CHEN J, ZHAO C, et al. Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: 103489.
    [9]
    WANG R C, LIU W, DING R K, et al. Switching control of semi-active suspension based on road profile estimation[J]. Vehicle System Dynamics, 2022, 60(6): 1972-1992.
    [10]
    KANG S W, KIM J S, KIM G W. Road roughness estimation based on discrete Kalman filter with unknown input[J]. Vehicle System Dynamics, 2019, 57(10): 1530-1544.
    [11]
    GORGES C, KEMAL Ö, LIEBICH R. Impact detection using a machine learning approach and experimental road roughness classification[J]. Mechanical Systems and Signal Processing, 2019, 117: 738-756.
    [12]
    QIN Y C, WEI C F, TANG X L, et al. A novel nonlinear road profile classification approach for controllable suspension system: simulation and experimental validation[J]. Mechanical Systems and Signal Processing, 2019, 125: 79-98.
    [13]
    WANG Z F, DONG M M, QIN Y C, et al. Suspension system state estimation using adaptive Kalman filtering based on road classification[J]. Vehicle System Dynamics, 2017, 55(3): 371-398.
    [14]
    QIN Y C, XIANG C L, WANG Z F, et al. Road excitation classification for semi-active suspension system based on system response[J]. Journal of Vibration and Control, 2018, 24(13): 2732-2748.
    [15]
    LIANG Guan-qun, ZHAO Tong, WANG Yan, et al. Road unevenness identification based on LSTM network[J]. Automotive Engineering, 2021, 43(4): 509-517, 628.
    [16]
    WU Xiao, SHI Wen-ku, CHEN Zhi-yong. Active suspension control based on interacting multiple model Kalman filter[J]. Automotive Engineering, 2023, 45(7): 1200-1211, 1253.
    [17]
    LI Yi-nong, ZHU Zhe-wei, ZHENG Ling, et al. Multi-objective control and optimization of active energy-regenerative suspension based on road recognition[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 129-137. doi: 10.19818/j.cnki.1671-1637.2021.02.011
    [18]
    KRATH J, SCHVRMANN L, VON KORFLESCH H F O. Revealing the theoretical basis of gamification: a systematic review and analysis of theory in research on gamification, serious games and game-based learning[J]. Computers in Human Behavior, 2021, 125: 106963.
    [19]
    WANG K F, GOU C, DUAN Y J, et al. Generative adversarial networks: introduction and outlook[J]. IEEE/CAA Journal of Automatica Sinica, 2017, 4 (4): 588-598.
    [20]
    CUI J J, LIU Y W, NALLANATHAN A. Multi-agent reinforcement learning-based resource allocation for UAV networks[J]. IEEE Transactions on Wireless Communications, 2020, 19 (2): 729-743.
    [21]
    LI N, YAO Y, KOLMANOVSKY I, et al. Game-theoretic modeling of multi-vehicle interactions at uncontrolled intersections[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 23(2): 1428-1442.
    [22]
    LAN Feng-chong, LIU Ying-jie, CHEN Ji-qing, et al. Study on motion planning of autonomous vehicles in cut-in scenes based on dynamic game algorithm[J]. Automotive Engineering, 2023, 45(1): 9-19.
    [23]
    WANG M, HOOGENDOORN S P, DAAMEN W, et al. Game theoretic approach for predictive lane-changing and car-following control[J]. Transportation Research Part C: Emerging Technologies, 2015, 58: 73-92.
    [24]
    HU Yi-kai, WANG Chun-xiang, YANG Ming. Decision making method of intelligent vehicles: a survey[J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 1035-1048.
    [25]
    DAI C H, ZONG C F, ZHANG D, et al. A bargaining game-based human-machine shared driving control authority allocation strategy[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(10): 10572-10586
    [26]
    LU Shao-bo, XIE Fei-fei, ZHANG Bo-han, et al. Human-vehicle cooperative game collision avoidance based on asymmetric potential fields[J]. Automotive Engineering, 2022, 44(10): 1484-1493.
    [27]
    WANG Gang, LI Kun-peng, JING Hui, et al. Parameter-free H∞ control of vehicle active suspension based on Q-learning[J]. Automotive Engineering, 2023, 45(12): 2260-2271.
    [28]
    LI Zhong-xing, SHEN An-cheng, JIANG Hui. Research on multi-agent game control system of an electronic air suspension[J]. Automotive Engineering, 2020, 42(6): 793-800.
    [29]
    CHEN Shi-an, GUAN Yu-liang, REN Jie-yu, et al. Mechanical characteristics test and nonlinear active controller design of energy-regenerative actuator for suspension[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 232-243. doi: 10.19818/j.cnki.1671-1637.2022.04.018
    [30]
    LI Zhong-xing, TANG Wei, HUANG Jian-yu, et al. Game control of multi-agent damper system for laterally interconnected air suspension[J]. Journal of Traffic and Transportation Engineering, 2018, 18(5): 130-139. doi: 10.19818/j.cnki.1671-1637.2018.05.013
    [31]
    WANG Long, HUANG Feng. An interdisciplinary survey of multi-agent games, learning, and control[J]. Acta Automatica Sinica, 2023, 49(3): 580-613.
    [32]
    ZHANG M H, JING X J, WANG G. Bioinspired nonlinear dynamics-based adaptive neural network control for vehicle suspension systems with uncertain/unknown dynamics and input delay[J]. IEEE Transactions on Industrial Electronics, 2021, 68(12): 12646-12656.
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (140) PDF downloads(9) Cited by()
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return