## 6 Conclusions  
In this paper, a hydrogen hybrid UAV energy system model, comprising proton exchange membrane fuel cells, lithium batteries, and hydrogen storage tanks, is developed to address the challenges of strong coupling and uncertainty in system operation. The primary objective is to minimize electrical power consumption and control costs for the hydrogen hybrid UAV. Electrical power is optimized by utilizing MPC method, while the DDPG algorithm is employed to achieve efficient energy scheduling optimization. This approach aims to minimize scheduling costs, ensure system sustainability, and account for  
Figure 11: Hydrogen fuel cell and SOC scheduling during 19–36 min. Figure 12: Hydrogen fuel cell and SOC scheduling during 346–360 min. operational constraints of the system components. The main conclusions of the study are as follows:  
1. The hydrogen hybrid UAV energy scheduling model developed in this study fully considers the system's operational state and continuous device scheduling, allowing for a comprehensive representation of the dynamic operational process of the system's devices;
2. The MPC method is used to achieve lower electrical power consumption for the multi-task hydrogen hybrid UAV, while the DDPG algorithm minimizes the energy scheduling cost;
3. Uncertainty is introduced into the minimum electrical power to simulate the uncertainty in the UAV's flight process. The DDPG algorithm demonstrates high real-time scheduling capability and system adaptability;
4. The combination of MPC and DDPG algorithms effectively solves the electrical energy scheduling problem for multi-task hydrogen fuel cell UAVs. While the MPC algorithm alone cannot address the system's uncertainty, and the DDPG algorithm alone cannot optimize the electrical power side, the combination of both algorithms achieves optimization of the electrical power side and efficient scheduling of electrical power. Future research will extend the proposed energy scheduling framework by integrating more advanced machine learning algorithms, such as reinforcement learning with attention mechanisms, multi-agent systems, or deep neural networks designed specifically for real-time decision-making in UAVs. These advanced algorithms will aim to improve the optimization of energy management in hydrogen hybrid UAVs, enhancing both the efficiency and robustness of the scheduling process under more dynamic and complex operational conditions. Additionally, real-world flight experiments will be conducted to validate the effectiveness of the proposed methods in practical settings. These experiments will focus on testing the energy scheduling framework across a range of flight missions, with the goal of demonstrating its applicability and reliability under various environmental factors, including variable weather conditions, payload variations, and flight trajectory changes. This phase of the research will provide critical insights into the real-time performance and scalability of the system, paving the way for the industrial implementation of energy management solutions for hydrogen-powered UAVs in commercial applications. Furthermore, we plan to collaborate with industry partners to explore the integration of the proposed energy scheduling framework into existing UAV platforms, with potential applications in surveillance, logistics, and environmental monitoring. This collaboration will help bridge the gap between theoretical advancements and real-world implementation, ensuring that the proposed solution can be effectively deployed in large-scale UAV systems. Conceptualization, H.L. and L.S. ; methodology, C.W. and L.S. ; software, C.W. and S.Y. ; validation, H.L., C.W., S.Y. and L.S. ; formal analysis, S.Y. ; investigation, H.L. and C.W. ; resources, H.Z. and B.L. ; data curation, Y.L. ; writing--original draft preparation, C.W. and S.Y. ; writing--review and editing, C.W. and S.Y. ; visualization, C.W. ; supervision, H.L. and L.S. ; funding acquisition, H.L. and L.S. All authors have read and agreed to the published version of the manuscript.