Trajectory Tracking Control Method via Simulation for Quadrotor UAVs Based on Hierarchical Decision Dual-Threshold Adaptive Switching

In complex 3D maneuvering tasks (e.g., post-disaster rescue, urban operations, and infrastructure inspection), the trajectories that quadrotors need to track are often complex—containing both gentle flight phases and highly maneuverable trajectory segments. Under such trajectory tracking tasks with the composite characteristics of “gentle-high maneuvering”, quadrotors face challenges of limited onboard computing resources and short endurance, requiring a balance between trajectory tracking accuracy, computational efficiency, and energy consumption. To address this problem, this paper proposes a lightweight trajectory tracking control method based on hierarchical decision-making and dual-threshold adaptive switching. Inspired by the biological “prediction–reflection” mechanism, this method designs a dual-threshold collaborative early warning switching architecture of “prediction layer–confirmation layer”: The prediction layer dynamically assesses potential risks based on trajectory curvature and jerk, while the confirmation layer confirms in real time the stability risks through an attitude-angular velocity composite index. Only when both exceed the thresholds, it switches from low-energy-consuming Euler angle control to high-precision geometric control. Simulation experiments show that in four typical trajectories (straight-line rapid turn, high-speed S-shaped, anti-interference composite, and narrow space figure-eight), compared with pure geometric control, this method reduces position error by 19.5%, decreases energy consumption by 45.9%, and shortens CPU time by 28%. This study not only optimizes device performance by improving trajectory tracking accuracy while reducing onboard computational load, but also reduces energy consumption to extend UAV endurance, and simultaneously enhances anti-disturbance capability, thereby improving its operational capability to respond to emergencies in complex environments. Overall, this study provides a feasible solution for the efficient and safe flight of resource-constrained onboard platforms in multi-scenario complex environments in the future and has broad application and expansion potential.