Problem
Research questions and friction points this paper is trying to address.
Nonlinear Dynamics
Aerodynamic Limits
Environmental Disturbances
Differential Flatness
Model Predictive Control
Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems
🤖 AI Summary
该研究针对固定翼无人机的轨迹跟踪问题，提出了一种结合微分平坦性和非线性模型预测控制的方法，并引入了风感知采样策略以提高鲁棒性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Nonlinear Model Predictive Control
Differential Flatness
Wind-aware Sampling
🔎 Similar Papers
No similar papers found.
