This study develops novel event triggered finite-time adaptive sliding-mode coordinated control method for networked hysteretic leaf spring suspension system subject to limited bandwidth, uncertainty sprung mass, dynamic constraints, and multiple control objectives. For inherent contradiction between body vibration and suspension chatter space, nonlinear filtering coordinated strategy with variable cut-off frequency is tailored to synthesize controlled variable composed of body and filtered tire displacements to realize smooth switch between hard and soft suspension under large/small suspension deformation. For the transmission congestion of control signals, event-triggered scheme relying on active force is designed to flexibly regulate command release interval to save communication resource and guarantee vibration suppression capability under abrupt road disturbance. To further improve convergence abilities of encapsulated variable and mitigate deterioration of performances from sparse control command, finite-time prescribed performance control is proposed to ensure transient and steady-state performance by accelerating convergence of controlled error to small region within preset time. For reconstructed active suspension system with unknown body weight, step control inputs with estimated weight are generated by employing event-based adaptive sliding-mode control to improve ride comfort and enhance handling properties. Additionally, global stability of networked active suspension system is proved by Lyapunov theory. Finally, the effectiveness and benefits of the proposed control method in real leaf spring suspension system are demonstrated by the simulation and hardware-in-the-loop tests.


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    Title :

    Event Triggered Finite-Time Adaptive Sliding-Mode Coordinated Control of Uncertain Hysteretic Leaf Spring Suspension With Prescribed Performance


    Contributors:
    Zhang, Jinhe (author) / Ding, Fei (author) / Liu, Jie (author) / Lei, Fei (author) / Wang, Yafei (author) / Wei, Chongfeng (author)


    Publication date :

    2025-02-01


    Size :

    4928506 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English