In this paper, the tracking control of the air-breathing hypersonic vehicle with model parameter uncertainties and actuator faults is studied. Firstly, a high-order linearization model is used to build an adaptive terminal sliding mode that eliminates chattering and provides increased robustness for unknown disturbances in the system. Second, a fault-tolerant control method mixing the radial basis function neural network with adaptive sliding mode control is suggested, with the addition of a hyperbolic tangent function to avoid controller input saturation. Finally, the stability of the controller is proved strictly by the Lyapunov theory, and the robustness and effectiveness of the controller are further verified by numerical simulations of the longitudinal model of the hypersonic vehicle.


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    Title :

    Fault-Tolerant Control of Hypersonic Vehicle Using Neural Network and Sliding Mode


    Contributors:
    Peng Cui (author) / Changsheng Gao (author) / Wuxing Jing (author) / Ruoming An (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown