Spiking Neural Network (SNN) control systems have demonstrated advantages over conventional Artificial Neural Networks (ANNs) in energy efficiency and data paucity. In this study, we introduce a SNN-based controller designed within the Neural Engineering Framework (NEF) for the stabilization and trajectory tracking of a quad rotorcraft Unmanned Aircraft System (UAS). The controller’s effectiveness is evaluated by simulation, successfully achieving tasks such as take-off, altitude regulation, and circular trajectory tracking. Our results provide insights into the practical viability and potential of this neuromorphic approach in real-world UAS applications.


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    Title :

    Spiking Neural Network-based Flight Controller




    Publication date :

    2024-04-23


    Size :

    4035359 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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