月面着陆动力下降段最优轨迹序列凸优化方法

针对月面着陆器动力下降制导过程中,时变惯性加速度和重力加速度难以估计与补偿等问题,提出一种基于序列凸优化的在线制导算法。在考虑月面曲率及月球自转的着陆器动力学建模基础上,首先对模型及约束条件进行凸化,得到一个二阶锥规划(SOCP)问题；然后对经典序列凸优化进行了改进,对时变加速度剖面予以实时估计和补偿,提升了现有优化算法的性能,使着陆器在尽可能节约燃料的前提下实现高精度着陆。仿真结果表明,与经典的显式制导律相比,所提出的算法在动力下降段燃料消耗更少。由多种位置偏差下的打靶分析结果可知,所提出的算法均能满足性能指标要求；即使起始位置存在±2500 m的较大波动时,仍能以高精度的速度、位置完成动力下降制导。

Sequential Convex Optimization Method for Lunar Landing During Powered Descent Phase

Aiming at the problem that it is difficult to estimate and compensate the time varying inertial acceleration and gravitational acceleration during the powered descent phase of the lunar lander, an online guidance algorithm based on sequential convex optimization is proposed. Firstly, based on the lander dynamic model considering the lunar surface curvature and lunar rotation, the model and constraints are convexified to obtain a second order cone programming (SOCP) problem. Then, the classical convex optimization method is improved to estimate and compensate the time varying acceleration profile in real time, which improves the performance of the existing optimization algorithm and enables the lander to land with high precision under the premise of saving fuel as much as possible. The simulation results show that compared with the classical explicit guidance law, the proposed algorithm consumes less fuel in the powered descent phase. The shooting analysis results under various position deviations show that the proposed algorithm can meet the performance requirements. Even if the initial position has a large fluctuation of ±2500 m, it can still complete the powered descent guidance with high precision speed and position.