Print Email Facebook Twitter Evolved Neuromorphic Altitude Controller for an Autonomous Blimp Title Evolved Neuromorphic Altitude Controller for an Autonomous Blimp Author Gonzalez Alvarez, Marina (TU Delft Aerospace Engineering) Contributor Dupeyroux, J.J.G. (mentor) de Croon, G.C.H.E. (graduation committee) Corradi, Federico (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering | Control & Simulation Date 2021-11-02 Abstract Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By mimicking the biological process for transferring information between neurons using spikes or impulses, they allow for low power consumption and asynchronous event-driven processing. In this work, we propose an evolved altitude controller based on a SNN for an airship which relies solely on the sensory feedback provided by an airborne radar sensor. Starting from the design of a a lightweight, low-cost, open-source airship, we also present a low-control-effort SNN architecture, an evolutionary framework for training the network in a simulated environment, and a control scheme for ameliorating the performance of the system in real-world scenarios. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network (ANN) and a linear controller (PID). The results show an accurate tracking of the altitude command while ensuring efficient management of the control effort. The main contributions of this work are presented in the scientific paper, corresponding to Part I of the document. Besides the research on altitude control based on SNNs and their comparison with an ANN and a PID, this thesis includes an in-depth review of the relevant literate on the main topics covered, in Part II. Finally, a detailed explanation of the methodologies used, the conclusions and recommendations for future work are proposed in Part III. Subject NeuromorphicSpiking Neural NetworksMicro Air VehicleRobot ControlAutonomous System To reference this document use: http://resolver.tudelft.nl/uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9 Part of collection Student theses Document type master thesis Rights © 2021 Marina Gonzalez Alvarez Files PDF Report.pdf 26.07 MB Close viewer /islandora/object/uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9/datastream/OBJ/view