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    This study introduces an adaptive backstepping controller for cloud-aided nonlinear active suspension systems. The novel approach significantly enhances vehicle suspension performance, improving it by over 80% compared to passive systems.

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    Area of Science:

    • Control Engineering
    • Automotive Engineering
    • Mechatronics

    Background:

    • Active vehicle suspension systems are crucial for ride comfort and handling.
    • Nonlinearities, uncertainties, and friction pose significant challenges in suspension control.
    • Cloud-aided systems offer potential for enhanced, adaptive control strategies.

    Purpose of the Study:

    • To develop an adaptive backstepping control strategy for a cloud-aided nonlinear active full-vehicle suspension system.
    • To model a novel nonlinear active suspension system incorporating uncertainties and nonlinear components.
    • To validate the controller's effectiveness in improving suspension performance.

    Main Methods:

    • A novel nonlinear active suspension model was established, considering uncertain parameters, friction, and nonlinear springs/dampers.
    • An adaptive backstepping control strategy was designed to address system nonlinearities and uncertainties.
    • A cloud-aided framework was utilized for remote controller updates using stored system and road data.

    Main Results:

    • The proposed adaptive backstepping controller demonstrated effectiveness in simulation.
    • The controller significantly improved suspension performance compared to passive systems.
    • Performance enhancements exceeding 80% were observed in the 7-degree-of-freedom full vehicle model.

    Conclusions:

    • The developed adaptive backstepping control scheme is effective for cloud-aided nonlinear active suspension systems.
    • Cloud integration enables dynamic controller updates, enhancing adaptability.
    • The proposed method offers substantial improvements in vehicle suspension performance and safety.