【摘要】 针对环境摄动和推力受限下轨道交会任务中传统算法计算复杂度高、初值敏感的问题，提出基于强化学习的燃料最优轨道交会控制方法，适用于低地球轨道卫星编队初始化与重构。该方法采用相对轨道根数描述卫星相对运动，建立考虑J₂摄动的多脉冲-连续推力控制模型。通过将约束优化问题离散化为连续决策过程，应用SAC强化学习算法训练智能体生成燃料最优交会轨迹，并采用解析法修正终端轨道偏差。结合蒙特卡洛树搜索优化编队重构策略，选择成员卫星目标轨道位置以降低总燃料消耗。仿真结果表明，该方法可精确高效生成燃料最优方案，计算时间与性能指标优于传统方法，具备良好泛用性和鲁棒性，为大规模卫星编队轨道任务分析提供有效解决方案。更多还原

【Abstract】 In the context of orbital rendezvous problems with environmental perturbations and thrust limitations, a fuel-optimal orbital rendezvous control method based on reinforcement learning is used to address issues of high computational complexity and sensitivity to initial values in traditional algorithms. This method can be employed for the initialization and reconfiguration of low Earth orbit satellite formations. The method describes the relative motion between satellites using relative orbital elements and establishes multi-impulse and continuous-thrust control models considering J₂ perturbation. By discretizing the constrained optimization problem into a sequential decision process, a soft actor-critic（SAC） reinforcement learning algorithm is used to train the agent for generating fuel-optimal rendezvous trajectories, and an analytical method is employed to correct terminal orbit deviations. Meanwhile, a Monte Carlo tree search（MCTS） algorithm is integrated to optimize the formation reconfiguration strategy, selecting the target orbital positions of member satellites to reduce total fuel consumption. Simulation results show that the proposed method can accurately and efficiently generate fuel-optimal rendezvous and formation reconfiguration schemes, with computational time and performance metrics superior to traditional optimization methods. It also demonstrates good generality and robustness, providing an effective solution for large-scale satellite formation mission analysis.更多还原