Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances. Differential flatness offers a principled way to generate fast, feasible trajectories, but its use has largely been confined to model-free controllers, which lack predictive capabilities and demand tuning. In this paper, we propose a unified framework that integrates differential flatness-based trajectory generation with Nonlinear Model Predictive Control (NMPC), combining computationally efficient planning with predictive, constraint-aware control. To further improve robustness, we introduce a wind-aware sampling strategy embedded within the NMPC framework, enabling the generation of dynamically feasible reference trajectories that proactively account for wind disturbances while strictly enforcing aerodynamic and control input constraints. We validate the proposed framework through extensive simulations and real-world flight experiments, demonstrating improved tracking accuracy and robustness for complex trajectories, particularly when using the proposed wind-aware sampling strategy under strong wind conditions.
Figures & tables
Figure 2: Overview of the proposed NMPC for trajectory tracking.
Figure 3: Reference frames and aerodynamic angles.
Figure 4: Wind sampling strategy overview.
Figure 5: NMPC tracking results with wind up to 4\penaltym/s .
Constraint
Bounds
(px,py,pz) [m]
[−104,104]
(vx,vy,vz) [m/s]
[5,25]
(ωx,ωy,ωz) [rad/s]
[−10,10]
δa [deg]
[−30,30]
δe [deg]
[−30,30]
δr [deg]
[−30,30]
Table I : NMPC parameters and constraints.
Figure 6: Airspeed tracking performance with and without wind-aware sampling in 7m/s wind.
Figure 7: Airspeed tracking with wind adjustment (top) vs Airspeed tracking without wind adjustment (bottom).
NMPC with sampling
NMPC w/o sampling
∥wI∥[\mathrm{m}\mathrm{/}\mathrm{s}]
Mean Va error [\mathrm{m}\mathrm{/}\mathrm{s}]
Max AoA [\mathrm{\SIUnitSymbolDegree}]
Mean Va error [\mathrm{m}\mathrm{/}\mathrm{s}]
Max AoA [\mathrm{\SIUnitSymbolDegree}]
2
0.874
4.949
1.274
5.490
4
0.867
6.383
2.317
7.685
6
0.541
6.839
3.408
11.016
8
0.379
8.180
4.514
15.514
10
0.592
9.389
5.404
20.967
Table II : NMPC airspeed tracking and AoA performance
Figure 8: Recovery from unfeasible reference trajectory.
Figure 9: NMPC tracking results (Real-world): (a) position tracking and (b) thrust and velocity tracking with error highlighting.
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.
Erchao Rong, Zihao Liu, Junning Liang +5
School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China
Trajectory tracking performance of Uncrewed Aerial Vehicles (UAVs) degrades during an agile high-speed flight due to the depletion of the battery and subsequent loss of maximum available thrust. In applications such as drone racing, this leads to a failure to complete the race due to possible collisions with obstacles. In this paper, we present a novel method for integrating battery and propulsion system models into a Nonlinear Model Predictive Controller (NMPC) framework to enable real-time prediction of the voltage, current, power, and maximum available thrust of the platform. Our proposed approach achieves lower trajectory tracking error as a result of its real-time thrust awareness, and with the help of trajectory replanning, it allows the UAV to fly in time-optimal regime throughout the mission. The accuracy of this proposed model was verified in real-world flight experiments, while the effectiveness of the replanning algorithm was evaluated in simulation. By the end of the battery capacity, compared to an unaware controller, our novel controller achieved a 25% reduction in mean position error without replanning, and an 88 % reduction with replanning.
Parakh M. Gupta, Matej Mihulka, Matej Novosad +2
Multi-robot Systems Group, Czech Technical University in Prague, Czech Republic
Gliding offers small fixed-wing UAVs extended endurance and silent operation but requires accurate energy management, especially under wind disturbances and obstacle constraints. Traditional Total Energy Control Systems based controllers regulate the trade between potential and kinetic energy reactively, often requiring fine-tuning and trim-conditions knowledge. In this work, we shift the regulation to the planning level and present a nonlinear, multi-cost trajectory planner for small UAV gliders. The method generates C3 continuous trajectories based on Bernstein polynomials, mapped into control commands through differential flatness, and re-planned online to match experimentally derived sink polar curves. A simulated netto variometer is integrated into the optimization to estimate air mass motion, constraining the glide to energy-balanced states. Consecutive gliding trajectories are linked by cruising segments computed through trajectories initialized on Dubins path-based waypoints, enabling hybrid missions that combine powered and unpowered flight. The approach is validated in CFD simulations and real-world experiments with a fixed-wing platform, showing reliable stabilization of sink rate, airspeed, and glide ratio under wind gusts and in presence of obstacles.
Luca Morando, Nishanth Bobbili, Giuseppe Loianno
New York University, NY 10012, USA · University of California Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA 94720, USA