ABSTRACT Unmanned Aerial Vehicles (UAVs) play a crucial role in search and rescue (SAR) operations, surveillance, amongst others, but their deployment in adverse weather conditions, mainly high‐winds, remains a challenge. The proposed hybrid control system serves as a step toward more robust and intelligent UAV solutions capable of operating in extreme weather conditions. The advent of artificial intelligence has encouraged the move towards the development of adaptive systems, which rely on static datasets and limited generalization in complex environments, encouraging the shift towards dynamic, real‐time learning methods. This paper presents a hybrid control strategy for enhancing UAV stability in strong wind conditions by integrating Linear Quadratic Regulator (LQR) control, Particle Swarm Optimization (PSO), and Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The proposed approach is implemented on the Field‐based Autonomous LiDAR Control for Obstacle Navigation (FALCON) Digital Twin, developed at Ulster University, enabling simulation and testing of wind disturbances as faults. The performance and complexity analysis of the strategies indicates that the proposed DDPG to LQR‐PSO controller (HyLPD) offers superior stability, effectively mitigating wind‐induced deviations while maintaining manageable computational complexity. Results show that while LQR‐PSO achieves a 40% performance improvement with a low computational cost, the HyLPD method further enhances system robustness, reducing overshoot and improving settling times under turbulent conditions without extensively labeled data. However, the increased computational demand of hybrid controllers suggests a trade‐off between adaptability and real‐time feasibility for UAV deployment. This study highlights the importance of hybrid control frameworks in UAV applications, particularly for SAR, where resilience to environmental disturbances is critical.
Rose et al. (Thu,) studied this question.