Autonomous Carrier Landing Control Strategy for VTOL UAVs Based on Deep Deterministic Policy Gradient Reinforcement Learning

Abstract

Autonomous shipboard recovery of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) is characterized by tight terminal constraints, rapidly varying wind disturbances, and deck motion induced by sea states. These factors lead to significant model uncertainty and render purely model-based designs brittle when the operating envelope broadens.
This paper develops an autonomous carrier-landing control strategy based on Deep Deter- ministic Policy Gradient (DDPG) for continuous control. Carrier recovery is formulated as a constrained Markov decision process (CMDP) using a deck-relative state representation and an action space consistent with common inner-loop attitude/thrust architectures. To improve training stability and reduce unsafe behaviors, we propose (i) a structured reward with explicit terminal touchdown constraints, (ii) constraint-aware termination and curriculum scheduling across approach phases, and (iii) domain randomization over aerodynamics, actuator dynamics, sensing latency/noise, wind gusts, and deck motion.
Comprehensive simulation studies demonstrate that the learned policy achieves higher land- ing success rates and lower touchdown dispersion than tuned PID guidance–control baselines under a wide range of perturbations. We further report ablations on reward terms and random- ization ranges, and discuss practical considerations for sim-to-real transfer.