Peaking into the Black-box: Prediction Intervals Give Insight into Data-driven Quadrotor Model Reliability

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Ensuring the reliability and validity of data-driven quadrotor model predictions is essential for their accepted and practical use. This is especially true for grey- and black-box models wherein the mapping of inputs to predictions is not transparent and subsequent reliability notoriously difficult to ascertain. Nonetheless, such techniques are frequently and successfully used to identify quadrotor models. Prediction intervals (PIs) may be employed to provide insight into the consistency and accuracy of model predictions. This paper estimates such PIs for polynomial and Artificial Neural Network (ANN) quadrotor aerodynamic models. Two existing ANN PI estimation techniques - the bootstrap method and the quality driven method – are validated numerically for quadrotor aerodynamic models using an existing high-fidelity quadrotor simulation. Quadrotor aerodynamic models are then identified on real quadrotor flight data to demonstrate their utility and explore their sensitivity to model interpolation and extrapolation. It is found that the ANN-based PIs widen considerably when extrapolating and remain constant, or shrink, when interpolating. While this behaviour also occurs for the polynomial PIs, it is of lower magnitude. The estimated PIs establish probabilistic bounds within which the quadrotor model outputs will likely lie, subject to modelling and measurement uncertainties that are reflected through the PI widths.
Original languageEnglish
Title of host publicationAIAA SciTech Forum 2023
Number of pages21
ISBN (Electronic)978-1-62410-699-6
Publication statusPublished - 2023
EventAIAA SCITECH 2023 Forum - National Harbor, MD & Online, Washington, United States
Duration: 23 Jan 202327 Jan 2023


ConferenceAIAA SCITECH 2023 Forum
Country/TerritoryUnited States
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