Dual-Regime Hybrid Aerodynamic Modeling of Winged Blimps With Neural Mixing

TL;DR

This paper introduces a hybrid aerodynamic modeling framework for winged blimps that combines physics-based models with neural network mixing to accurately capture different flight regimes and their transitions.

Contribution

It presents a novel hybrid modeling approach using a neural mixer with physics regularization, enabling smooth regime transitions and improved accuracy over existing models.

Findings

Outperforms single-model and baseline methods in flight trajectory prediction

Validated on 1,320 real-world trajectories across diverse configurations

Demonstrates robustness and practicality for winged blimp aerodynamic modeling

Abstract

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.