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GUIDANCE SCHEME FOR AUTONOMOUS ELECTRIC-PROPELLED SPACECRAFT

机译：自主式电动航天器的制导方案

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In recent years, many interplanetary trajectories have been designed taking advantage of electric engines. These engines are more efficient in terms of fuel consumption than classical chemical thrusters due to the higher specific impulse, but they give lower thrust magnitude. Several techniques have been developed to obtain optimum trajectories with low-thrust propulsion. However, few low-thrust guidance schemes have been investigated to fly the reference optimum trajectories. The considered guidance problem applied to the low-thrust arcs consists compensating the state vector deviation with respect to the nominal trajectory at a certain instant selected as the guidance objective. This strategy allows a trajectory to be followed with multiple thrust arcs and also to leave the SC in the best condition for any encounter (swing-by, flyby, or rendezvous). Within GMV, the DeepSpacel experience has been taken as a starting point towards the implementation of a refined algorithm. The applicability of the algorithm to different interplanetary low-thrust trajectories, independently of the optimization technique they result from, is feasible after pre-processing of the given trajectory. This pre-processing transforms any given thrust profile to a thrust law defined by a finite set of control variables. This set of control variables defines a discretized control vector to be optimized for guidance purposes. The attitude constraints are considered in the guidance scheme as restrictions in the maximum angular separation between the corrected thrust direction and the nominal one. The active constraints are considered in the new optimization process projecting the corrected thrust direction onto the boundary defined by the angular limit. Simulations are carried out to test the performances of the algorithms to very different missions such as SMART-1 and BepiColombo. The initial deviation in the SC state vector (in the phase-space) and the mismodeled forces will lead to a final state vector very far from the nominal final state. Parametric analysis allows assessing the stability and robustness of the schemes and the sensitivity to certain parameters.
机译：近年来，已经利用电动发动机设计了许多行星际轨道。与传统的化学推进器相比，这些发动机由于具有更高的比冲，因此在燃油消耗方面更为高效，但其推力幅度却较低。已经开发了几种技术来获得具有低推力推进的最佳轨迹。然而，很少有人研究低推力制导方案来飞越参考最优轨迹。应用于低推力弧线的考虑的制导问题包括补偿在选定为制导目标的某一时刻相对于名义轨迹的状态矢量偏差。这种策略允许轨迹带有多个推力弧，并使SC保持最佳状态以应对任何相遇（摆动，飞越或会合）。在GMV中，DeepSpacel的经验已被用作实现改进算法的起点。在对给定轨迹进行预处理之后，该算法可应用于不同的行星际低推力轨迹，而与它们产生的优化技术无关。该预处理将任何给定的推力曲线转换为由有限的一组控制变量定义的推力定律。这组控制变量定义了离散化的控制向量，可以将其优化以用于指导目的。在制导方案中，姿态约束被视为对校正后的推力方向与标称方向之间最大角度间隔的限制。在新的优化过程中考虑了主动约束，将校正后的推力方向投影到角度限制所定义的边界上。进行仿真以测试算法在不同任务（例如SMART-1和BepiColombo）上的性能。 SC状态向量（在相空间中）的初始偏差和模型错误的力将导致最终状态向量与标称最终状态相差很远。参数分析允许评估方案的稳定性和鲁棒性以及对某些参数的敏感性。

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