Abstract While high-efficiency propulsion techniques are enabling new mission concepts in deep space exploration, their limited thrust capabilities necessitate long thrusting arcs and make spacecraft more susceptible to missed thrust events. To correct for such mishaps, most spacecraft require updated trajectories that are relayed from Earth. While this solution is viable for spacecraft near Earth, in deep space, where one-way communication time is measured in hours, a delay in transmission may prolong the time of flight or result in a complete loss of mission. Such problems can be alleviated by increasing the spacecraft's onboard autonomy in guidance. This paper demonstrates how a computationally lightweight neural network can map the spacecraft's state to a near-optimal control action, autonomously guiding a spacecraft within different astrodynamic regimes and optimality criteria. The neural network is trained using supervised learning and datasets comprised of optimal state-action pairs, as determined through traditional direct and indirect methods. Additionally, the neural network-designed solutions retain optimality and time of flight corresponding to traditional trajectories. Finally, the same neural networks can autonomously correct for most missed thrust events encountered on long-duration low-thrust trajectories. The presented results provide a path for mitigating risks associated with the use of high-efficiency low-thrust propulsion techniques.

中文翻译：

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天体动力学中的神经网络优化控制：在错过推力问题中的应用mytitlenote

摘要 虽然高效推进技术在深空探索中实现了新的任务概念，但其有限的推力能力需要长推力弧并使航天器更容易受到错过推力事件的影响。为了纠正此类事故，大多数航天器都需要更新从地球转播的轨迹。虽然这种解决方案对于靠近地球的航天器是可行的，但在深空，单向通信时间以小时为单位，传输延迟可能会延长飞行时间或导致任务完全失败。这些问题可以通过增加航天器的机载自主制导来缓解。本文展示了计算量轻的神经网络如何将航天器的状态映射到接近最优的控制动作，在不同的天体动力学状态和优化标准内自主引导航天器。神经网络使用监督学习和由最佳状态-动作对组成的数据集进行训练，这些数据集是通过传统的直接和间接方法确定的。此外，神经网络设计的解决方案保留了与传统轨迹相对应的最优性和飞行时间。最后，相同的神经网络可以自动纠正在长时间低推力轨迹上遇到的大多数错过的推力事件。所呈现的结果为降低与使用高效低推力推进技术相关的风险提供了途径。通过传统的直接和间接方法确定。此外，神经网络设计的解决方案保留了与传统轨迹相对应的最优性和飞行时间。最后，相同的神经网络可以自动纠正在长时间低推力轨迹上遇到的大多数错过的推力事件。所呈现的结果为降低与使用高效低推力推进技术相关的风险提供了途径。通过传统的直接和间接方法确定。此外，神经网络设计的解决方案保留了与传统轨迹相对应的最优性和飞行时间。最后，相同的神经网络可以自动纠正在长时间低推力轨迹上遇到的大多数错过的推力事件。所呈现的结果为降低与使用高效低推力推进技术相关的风险提供了途径。