The Deep Deterministic Policy Gradient (DDPG) algorithm is a reinforcement learning algorithm based on the Actor-Critic framework, suitable for continuous action-space control and scheduling, as it directly outputs deterministic actions instead of a probability distribution [13; 14]. For instance, [PERSON] et al. [15] discussed the application of reinforcement learning (RL) in energy management for fuel cell hybrid energy systems (FCHEVs), emphasizing gaps in existing literature regarding training environments, reward function settings, and RL agent evolution. The authors highlight that reward functions are typically derived from optimization objectives, with additional penalties/rewards introduced to improve training outcomes. Although RL-based energy management strategies (EMSs) outperform conventional approaches when trained under specific conditions, they struggle with adaptability in unknown scenarios--a challenge that can be mitigated by training with multi-operating conditions. Advanced RL methods like DQNs, DDPGs, and Double DQNs have been shown to address issues like discretized state spaces and Q-function overestimation, offering better performance than basic RL algorithms. Future research directions include leveraging machine learning to model FCHEV dynamics more accurately and exploring novel RL algorithms to address current limitations. Additionally, [PERSON] et al. [16] compared the ECMS energy management strategy with the Adaptive-ECMS (A-ECMS) learning-based equivalent fuel consumption strategy, showing fuel consumption reductions of 0.96% and 1.37% under NEDC and CHTC-LT conditions, respectively. While rule-based and optimization-based methods exhibit robust scheduling performance in specific scenarios, they often fall short in adapting to uncertainties. Existing rule-based and optimization-based methods perform well in specific scenarios but struggle with complex and uncertain environmental changes. Rule-based methods rely on predefined rules and logical frameworks designed for known or simple situations. These methods allocate energy by comparing real-time variables against set thresholds, working effectively under stable conditions. However, when faced with uncertainties like drastic environmental fluctuations, sudden task changes, or dynamic vehicle performance, rule-based systems may lack the flexibility to adapt, leading to decreased efficiency or energy waste. Optimization-based methods, though capable of finding optimal solutions through mathematical models and constraints, also have limitations. They rely on known system models and conditions, so if the environment or system dynamics change (e.g., battery degradation or varying wind speeds), these methods may fail to deliver real-time optimal solutions. Additionally, many optimization algorithms are computationally intensive, particularly in complex, multi-objective problems, making them unsuitable for real-time applications. Furthermore, optimization methods typically assume that system changes can be predicted, but in practice, environmental and system behaviors often vary unpredictably, reducing the effectiveness of these methods in uncertain and dynamic environments. In contrast, learning-based methods excel in adaptability and generalization, making them well-suited to address complex models, high nonlinearity, and multiple environmental uncertainties. Therefore, this paper combines the Model Predictive Control (MPC), an optimization-based method, with the DDPG algorithm, a learning-based method, to achieve optimal power scheduling and efficient energy management for hydrogen-powered hybrid UAVs. The main contributions of this study are listed as follows:  
1. By adopting the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm, the issue of insufficient adaptability in existing methods under uncertain environments can be effectively addressed.