Cascaded TD3-PID Hybrid Controller for Quadrotor Trajectory Tracking in Wind Disturbance Environments
For quadrotor control practitioners, this work offers an incremental improvement by hybridizing PID and reinforcement learning with a disturbance observer to enhance tracking under wind disturbances.
This paper proposes a cascaded hybrid controller combining PID for altitude/attitude and an enhanced TD3 agent for horizontal-position control, achieving more accurate and robust quadrotor trajectory tracking under wind disturbances compared to baseline methods in simulations and real-world tests.
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 altitude and attitude control, a hybrid disturbance observer (HDOB) using low-pass and exponential moving average filtering is embedded in the control loops. The proposed TD3 enhancements are verified through ablation studies, and both numerical simulations and real-world flight tests on the quadrotor platform demonstrate that the proposed method achieves more accurate and robust trajectory tracking under wind disturbances than baseline approaches.