Tail-sitter UAV flies precise routes without airframe data
Aerodynamic Prior-Free Coordinated Trajectory Generation and Tracking Control for a Tail-Sitter UAV
Robotics
Summary
Flying a special type of drone called a tail-sitter is tricky because it changes how it flies so much, making control complicated. The authors created a method that plans and controls the path of this drone without needing detailed information about its specific shape or aerodynamics. Their technique works well across all flight modes and was tested both in simulations and real windy conditions. This helps these drones fly accurately without long, costly tests or models beforehand.
What this means in practice
- •For drone developers: Create control systems for tail-sitter drones that maintain accurate flight paths without prior aerodynamic testing of each airframe.
- •For unmanned aerial vehicle operators: Run tail-sitter UAVs reliably in changing wind and flight conditions without needing extensive aerodynamic calibration.
Authors
Erchao Rong, Zihao Liu, Junning Liang, Jianguo Wang, Xiao Jie, Haoran Fu, Ziliang Chen, Ximin Lyu
Abstract
This paper presents a coordinated trajectory generation and tracking control framework for a tail-sitter unmanned aerial vehicle (UAV), which does not require aerodynamic priors identified for a specific airframe while addressing the challenge of flight control under highly nonlinear aerodynamics across the full flight envelope. The core innovation lies in employing phase-specific aerodynamic modeling strategies for planning and tracking, tailored to their distinct functional characteristics, without requiring airframe-specific aerodynamic priors. Specifically, the phi-theory model under coordinated flight is employed to derive an analytic differential flatness mapping, and a simplified but locally accurate model is established for predictive control to enable real-time aerodynamic parameter estimation. The proposed framework is evaluated extensively through both simulation and challenging real-world flight tests under mild wind conditions, showing high-precision tracking and adaptability across the tested aerodynamic conditions. To the best of our knowledge, this is the first real-world demonstration of accurate trajectory tracking over tested flight regimes spanning the full envelope of a tail-sitter UAV without relying on aerodynamic identification campaigns. The source code of our framework is available at: https://github.com/SYSU-HILAB/AP-PnC.