Robust Quadrotor Trajectory Tracking Under Multimodal Wind Disturbances via Residual-Aware Deep Reinforcement Learning

Abstract

Robust quadrotor trajectory tracking under wind disturbances is challenging because real outdoor wind is multimodal with time-varying and heavy-tailed characteristics, whereas existing solutions suffer from insufficient disturbance observability and poor out-of-distribution robustness. To this end, this paper presents a robust quadrotor trajectory-tracking method based on residual-aware deep reinforcement learning for multimodal wind disturbances. The proposed method augments a standard tracking policy with a compact online acceleration-residual feature, which provides disturbance-related information without requiring an explicit wind sensor, a full disturbance observer, or a long-history recurrent estimator. To reduce excessive dependence on wind-specific temporal patterns, a residual-input regularization term is introduced during policy optimization. In addition, a tail-risk-aware reward is designed to balance nominal tracking accuracy, control smoothness, and suppression of large tracking deviations. The proposed method is evaluated under in-distribution wind, held-out out-of-distribution wind, and measured real-wind disturbances. The results show that the proposed method achieves the most balanced robustness under multimodal wind conditions compared with baselines.