Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment

A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural Engineering Framework (NEF), SNN-...

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Bibliographic Details
Main Authors: Garcia A., Omar A., Arana, Diego Chavez, Espinoza, Eduardo S., Scola, Ignacio Rubio, Garcia Carrillo, Luis Rodolfo, Sornborger, Andrew T.
Format: Conference Proceeding
Language:English
Subjects:
Aerospace simulation
Heuristic algorithms
Neuromorphics
Regulation
Spiking neural networks
Stability analysis
Trajectory tracking
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Summary:A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural Engineering Framework (NEF), SNN-based Proportional+Derivative (PD) controllers are designed for the translational and rotational dynamics of the UAS. An online Model in the Loop (MIL) evaluation scenario was implemented, showing that the proposed neuromorphic controllers are capable of stabilizing the quad rotorcraft UAS in both regulation and trajectory tracking tasks.
ISSN:2378-5861
DOI:10.23919/ACC60939.2024.10644419