Cascaded TD3-PID Hybrid Controller for Quadrotor Trajectory Tracking in Wind Disturbance Environments
Yukang Zhang, Shuqi Chai, Yuhang Zhang, Danlan Huang, and Quanbo Ge

TL;DR
This paper introduces a hybrid control framework combining PID and an enhanced TD3 reinforcement learning agent with disturbance observers for robust quadrotor trajectory tracking in windy conditions.
Contribution
It develops a cascaded control system integrating PID, an improved TD3 agent, and hybrid disturbance observers to enhance disturbance rejection and tracking accuracy.
Findings
The hybrid controller outperforms baseline methods in simulations.
Real-world tests show improved robustness under wind disturbances.
Ablation studies confirm the effectiveness of each component.
Abstract
This work presents a cascaded hybrid control framework for quadrotor trajectory tracking under nonlinear dynamics and external disturbances. In quadrotor systems, the altitude and attitude channels exhibit fast, structured dynamics that are well suited to reliable regulation, whereas horizontal-position control is more strongly affected by coupling effects, uncertainty, and disturbances, so that neither pure feedback control nor purely learning-based control alone is equally well suited to all channels. Accordingly, the proposed framework augments conventional proportional-integral-derivative (PID) stabilization for altitude and attitude control with an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) agent incorporating a multi-Q-network structure, thereby improving horizontal-position control under severe disturbances. To further strengthen disturbance rejection in…
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