A CCO-PPO Framework for Autonomous UAV Trajectory Tracking in Complex and Disturbed Environments

Accurate trajectory tracking is fundamental to the autonomous operation of unmanned aerial vehicles (UAVs) in complex tasks. While proximal policy optimization (PPO) has shown strong potential in UAV control, its performance is highly sensitive to hyperparameter configuration, and manual tuning is time-consuming due to complex interparameter coupling. This paper proposes CCO-PPO, a framework integrating the cuckoo catfish optimizer (CCO) with PPO for automatic hyperparameter optimization in UAV trajectory tracking. The problem is formulated as a Markov decision process with a 20-dimensional state space, and the CCO performs offline search over a four-dimensional hyperparameter space. Evaluated across seven test environments covering diverse trajectory geometries, wind disturbances, sensor noise, and large-scale scenarios, CCO-PPO achieves the lowest tracking error in all cases. Performance gains over baseline PPO increase monotonically with task complexity, reaching 18.8% under combined wind disturbance and sensor noise, with statistically significant advantages in 85.7% of pairwise comparisons against baseline PPO, SAC, and TD3. Ablation studies confirm that joint optimization of all four hyperparameters is essential under high-disturbance conditions, and comparisons with Bayesian optimization validate the CCO's superior cross-seed stability. These results demonstrate that metaheuristic hyperparameter optimization substantially enhances policy robustness in high-disturbance UAV trajectory tracking scenarios.