Revisiting UAV Dynamic Modeling Assumptions: A Critical Analysis of Simplifications and Their Operational Implications

Abstract

To achieve real-time computational efficiency, UAV control engineering has historically relied on ubiquitous simplifying assumptions, such as small-angle approximations, constant diagonal inertia matrices, and the neglect of gyroscopic precession. While these enable baseline stabilization, their validity is challenged by the demand for higher speeds and aggressive autonomous maneuvers. This conceptual paper provides a critical re-examination of these foundational assumptions, analyzing their theoretical origins and quantifying the “model-plant mismatch” they introduce across various flight regimes. We introduce the “Fidelity-Latency Trade-off,” a decision-making space where the benefits of model accuracy are weighed against the computational costs of real-time execution. The analysis identifies specific “failure modes” of simplified models, such as the inability of linear controllers to maintain stability during high-pitch transitions or errors introduced by neglecting rotor flapping during rapid yawing. By employing a sensitivity analysis framework, the paper maps the error bounds of each assumption to specific operational parameters, such as the vehicle’s Rossby number and structural stiffness-to-weight ratio. This allows researchers to determine the “minimum viable model complexity” required for a given mission profile, from steady-state surveys to high-G racing. Furthermore, the study discusses the implications for formal safety verification, arguing that simplified models may mask non-linear instabilities. The paper concludes with a call for a “hierarchical modeling” approach, providing a theoretical roadmap for the development of physics-informed autonomous systems that safely push the boundaries of aerial robotics.