Chinese Journal of Construction Machinery, Volume. 23, Issue 3, 410(2025)

Simulation study on model predictive control of vehicle active suspension based on RBF neural network

GU Suyi1 and JIANG Changhua2
Author Affiliations
  • 1School of Mechanoelectrical Engineering, Suzhou Vocational University, Suzhou 215104, Jiangsu, China
  • 2School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China
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    References(5)

    [4] [4] JI G, ZHANG L, CAI M, et al. Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions [J]. Scientific Reports, 2024, 14(1): 2-14.

    [5] [5] ZAHIRIPOUR S A. A logarithmic sliding mode controller for stochastic active suspension systems [J]. Transactions of the Institute of Measurement and Control, 2023, 45(12): 2340-2351.

    [8] [8] KIM J, LEE T, KIM C J, et al. Model predictive control of a semi-active suspension with a shift delay compensation using preview road information [J]. Control Engineering Practice, 2023, 137(5): 2-7.

    [10] [10] MA S, LI Y, TONG S. Research on control strategy of seven-DOF vehicle active suspension system based on co-simulation [J]. Measurement and Control, 2023, 56(7): 1251-1260.

    [12] [12] PATRA A K. A vehicle suspension system based on Kalman filtering model predictive control algorithm [J]. International Journal of Advanced Mechatronic Systems, 2021, 9(2): 55-65.

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    GU Suyi, JIANG Changhua. Simulation study on model predictive control of vehicle active suspension based on RBF neural network[J]. Chinese Journal of Construction Machinery, 2025, 23(3): 410

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    Paper Information

    Received: --

    Accepted: Aug. 25, 2025

    Published Online: Aug. 25, 2025

    The Author Email:

    DOI:10.15999/j.cnki.311926.2025.03.027

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