基于神经网络的月球垂直着陆最优制导
Neural-Network-Based Optimal Guidance for Lunar Vertical Landing
AI总结：
本文针对月球着陆器垂直着陆的最优制导问题，提出通过引入非负小正则化项处理最终姿态约束，并利用Pontryagin极小值原理建立参数化系统生成最优轨迹，训练神经网络近似飞行状态到制导命令的映射，数值算例验证了方法有效性。
摘要：
本文研究了月球着陆器垂直着陆的最优制导问题，目标是使燃料消耗最小化。垂直着陆施加了最终姿态约束，该约束被视为最终控制约束。为了处理这一约束，我们提出了一个非负的小正则化项来扩充原始成本泛函。这确保了根据Pontryagin极小值原理满足最终控制约束。通过利用最优性的必要条件，我们建立了一个参数化系统，该系统便于生成大量最优轨迹，这些轨迹包含从飞行状态到最优制导命令的非线性映射。随后，训练一个神经网络来近似这种映射。最后，给出了数值算例来验证所提出的方法。
英文摘要：
This paper addresses an optimal guidance problem concerning the vertical landing of a lunar lander with the objective of minimizing fuel consumption. The vertical landing imposes a final attitude constraint, which is treated as a final control constraint. To handle this constraint, we propose a nonnegative small regularization term to augment the original cost functional. This ensures the satisfaction of the final control constraint in accordance with Pontryagin's Minimum Principle. By leveraging the necessary conditions for optimality, we establish a parameterized system that facilitates the generation of numerous optimal trajectories, which contain the nonlinear mapping from the flight state to the optimal guidance command. Subsequently, a neural network is trained to approximate such mapping. Finally, numerical examples are presented to validate the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
