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Optimal strategy for low-thrust spiral trajectories using Lyapunov-based guidance

机译：基于Lyapunov的制导低推力螺旋轨迹的最优策略

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摘要

It is difficult and time-consuming to search the optimal low-thrust spiral trajectories. This is because the analytical solutions for long powered arcs are not available, and hundreds or even thousands of orbital revolutions are involved in transfer trajectories. This paper examines a smart guidance scheme based on Lyapunov feedback control which overcomes the difficulties found in control gains steering. In this guidance scheme, the artificial neural network is adopted to implement gains steering and the evolutionary algorithm is used as the learning algorithm for artificial neural network. In this paper, Earth J2 perturbation and Earth-shadow eclipse effects are considered. Finally, a comparison with solutions given by the literature demonstrates the effectiveness of the proposed method. Numerical simulation results show that the time-varying gains guidance scheme can decrease the transfer time with respect to constant gains cases.
机译：搜索最佳的低推力螺旋轨迹既困难又费时。这是因为无法提供用于长动力电弧的解析解决方案，并且转移轨迹涉及数百甚至数千个轨道公转。本文研究了一种基于Lyapunov反馈控制的智能制导方案，该方案克服了在控制增益操纵中发现的困难。在该指导方案中，采用人工神经网络实现增益控制，并采用进化算法作为人工神经网络的学习算法。在本文中，考虑了地球J2扰动和地球阴影的月食效应。最后，与文献给出的解决方案进行比较，证明了该方法的有效性。数值模拟结果表明，相对于恒定增益情况，时变增益指导方案可以减少传输时间。

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