Herein, we investigate the robust formation control problem for a group of unmanned aerial vehicles (UAVs) with system uncertainty. A hierarchical formation control strategy is introduced to ensure the uniform ultimate boundedness of each UAV’s reference tracking error. First, a group of saturated high-level virtual agents are defined to act as the trajectory planners that offer feasible position references to the actual UAVs. A sliding mode neural-based observer is then constructed to estimate the nonlinear uncertainty in the UAV model. Furthermore, sliding mode controllers are designed for both the position loop and the attitude loop of the UAV. To attenuate the chattering phenomenon in the control input, a saturated and smoothed differentiator is proposed along with an observation introduction function. The effectiveness of the proposed control scheme is validated by both the Lyapunov stability theory and numerical simulations based on a multiple-UAV system.


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    Title :

    Robust Hierarchical Formation Control of Unmanned Aerial Vehicles via Neural-Based Observers


    Contributors:
    Yang Fei (author) / Yuan Sun (author) / Peng Shi (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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