(Raw Data Set) Spiking Neural Network-based Flight Controller

Abstract

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.