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.
New York University, NY 10012, USA · University of California Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA 94720, USA