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A Hybrid PEM-GP Framework for Uncertainty-Aware System Identification of Quadcopters

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Accurate dynamic models play a central role in achieving reliable control of quadcopters. Classical system identification methods remain widely used, mainly because of their interpretability. However, they often fail to capture important nonlinear effects, especially in small-scale aerial platforms where such effects become more pronounced. Data-driven approaches offer a different perspective. They can represent complex nonlinear dynamics more effectively, but this comes at the cost of reduced interpretability and the absence of well-calibrated uncertainty estimates. In this work, we propose a

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