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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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Neural-Network-Based Adaptive Event-triggered Control for Spacecraft Attitude Tracking.

Weixing Liu, Yunhai Geng, Baolin Wu

    IEEE Transactions on Neural Networks and Learning Systems
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    This study introduces an adaptive event-triggered control scheme to reduce data transmission for spacecraft attitude tracking. The method uses a neural network to handle uncertainties, ensuring system stability with reduced communication load.

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    Area of Science:

    • Aerospace Engineering
    • Control Systems
    • Robotics

    Background:

    • Spacecraft attitude control is crucial for mission success.
    • Limited communication rates pose challenges for traditional control methods.
    • Inertia uncertainties and external disturbances affect control accuracy.

    Purpose of the Study:

    • To develop an adaptive event-triggered control scheme for spacecraft attitude tracking.
    • To reduce communication burden by transmitting data only at event-triggering instants.
    • To address inertia uncertainties and external disturbances using a neural network.

    Main Methods:

    • An adaptive event-triggered control scheme is proposed.
    • A radial basis function neural network (NN) is utilized to estimate uncertainties and disturbances.
    • The closed-loop system is formulated as an impulsive dynamical system to analyze stability.

    Main Results:

    • The event-triggered mechanism significantly decreases data transmission rates.
    • The neural network effectively handles inertia uncertainties and external disturbances.
    • Stability properties of the closed-loop system are successfully demonstrated through simulation.

    Conclusions:

    • The proposed adaptive event-triggered control scheme is effective for spacecraft attitude tracking under limited communication rates.
    • The integration of a neural network simplifies control and reduces computation.
    • The impulsive dynamical system formulation provides a robust stability analysis.