Winged blimps operate across distinct aerodynamic regimes that cannot be adequately captured by a single model. At high speeds and small angles of attack, their dynamics exhibit strong coupling between lift and attitude, resembling fixed-wing aircraft behavior. At low speeds or large angles of attack, viscous effects and flow separation dominate, leading to drag-driven and damping-dominated dynamics. Accurately representing transitions between these regimes remains a fundamental challenge. This paper presents a hybrid aerodynamic modeling framework that integrates a fixed-wing Aerodynamic Coupling Model (ACM) and a Generalized Drag Model (GDM) using a learned neural network mixer with explicit physics-based regularization. The mixer enables smooth transitions between regimes while retaining explicit, physics-based aerodynamic representation. Model parameters are identified through a structured three-phase pipeline tailored for hybrid aerodynamic modeling. The proposed approach is validated on the RGBlimp platform through a large-scale experimental campaign comprising 1,320 real-world flight trajectories across 330 thruster and moving mass configurations, spanning a wide range of speeds and angles of attack. Experimental results demonstrate that the proposed hybrid model consistently outperforms single-model and predefined-mixer baselines, establishing a practical and robust aerodynamic modeling solution for winged blimps.

翻译：飞艇在不同气动模态下运行，单一模型无法充分捕捉其特性。在高速与小攻角条件下，其动力学呈现升力与姿态的强耦合特性，类似于固定翼飞行器行为。在低速或大攻角条件下，粘性效应与流动分离占主导地位，导致阻力驱动与阻尼主导的动力学特征。准确表征这些模态间的转换仍是根本性挑战。本文提出一种混合气动建模框架，通过具有显式物理正则化的神经网络混合器，集成固定翼气动耦合模型与广义阻力模型。该混合器能在保留显式物理气动表征的同时实现模态间的平滑过渡。模型参数通过专为混合气动建模设计的三阶段结构化流程进行辨识。所提方法在RGBlimp平台上通过大规模实验验证，涵盖330组推进器与移动质量配置下的1,320条真实飞行轨迹，覆盖广泛的速度与攻角范围。实验结果表明，所提出的混合模型在各项性能上均优于单一模型与预定义混合器基线，为飞艇建立了实用且鲁棒的气动建模解决方案。