【摘要】 针对未来月面着陆动力下降段轨迹规划需综合考虑多性能指标的问题，提出一种对飞行轨迹先优化后决策的多目标轨迹规划方法.在多目标进化算法MOEA/D-AWA(multi-objective evolutionary algorithm based on decomposition with adaptive weight adjustment)的框架下对轨迹规划的多个指标进行分解，得到若干个单指标的子问题.将凸优化算法作为求解单目标轨迹优化子问题的底层算法，嵌套在MOEA/D-AWA的框架中，经过迭代优化获得一组动力下降段飞行轨迹，其构成多目标轨迹规划问题的帕累托最优解集.根据模糊决策理论对各个帕累托最优解对应的多个轨迹指标逐步降阶并进行综合评估，经过决策得到多指标约束下的飞行轨迹.仿真实验表明，该轨迹规划方法能够在综合多目标的情况下，优化获得一组动力下降轨迹集合，且能够根据不同任务要求从中决策出最优的动力下降段轨迹，可有效解决月面飞行器的多目标轨迹规划问题.更多还原

【Abstract】 Aiming at the problem that multiple performance indicators need to be considered comprehensively for the trajectory planning of the future lunar surface landing power descent, this paper proposes a multi-objective trajectory planning method that optimizes first and decides later. First, the multiple metrics of trajectory planning are decomposed under the framework of MOEA/D-AWA(multi-objective evolutionary algorithm based on decomposition with adaptive weight adjustment) to obtain several single-indicator sub-problems. After that, the convex optimization algorithm is designed as the underlying algorithm for solving the single-objective sub-problems. After iterative optimization, a set of powered descent segment flight trajectories can be obtained, which constitutes the Pareto optimal solution set. Finally, according to the fuzzy decision theory, the multiple trajectory indicators corresponding to each Pareto optimal solution are gradually downgraded and evaluated comprehensively, and the flight trajectory under multi-indicator constraint is obtained after the decision. The simulation results show that the trajectory planning method can optimize a set of power descent trajectories under the integrated multi-objective situation, and can decide the optimal power descent trajectories from them according to different mission requirements, which can effectively solve the multi-objective trajectory planning problem of the lunar vehicle.更多还原