According to the application requirement of sub-orbital reusable launch vehicles (SRLV), an integrated optimization method of trajectory/system parameters based on memory principles is studied to ensure the satellite is launched into orbit and SRLV can fly back safely. The memory-based method is a randomized searching method with global convergence, while the quality states of trial solutions per search are stored by memory cells. The algorithm uses the memorizing and forgetting rules to judge the states of trial solutions in every searching, taking the fuel minimization as optimization index. Three different searching strategies are utilized to realize the randomized searching and avoid falling into local-minimum problems, thus improve the searching speed. Simulation results show that the velocity error of the satellite is less than 2 m/s, the height error is less than 10 m, and the orbit inclination is less than 0.000 1°. The height error and the velocity error of the SRLV are less than 100 m and 5 m/s, respectively. Compared with traditional trajectory optimization methods, the proposed method is more suitable to solve complicated trajectory/parameters integrated optimization problems, which has faster searching speed and higher solution accuracy, and is beneficial to the application and popularization of the algorithm in engineering practice.%针对亚轨道可重复使用运载器(SRLV)的应用需求,在将卫星投送到预定轨道同时确保SRLV安全返回的前提下,对基于记忆原理的轨迹/总体参数一体化优化方法进行了研究.记忆优化算法是一种具有全局收敛性的随机搜索方法,每次搜索的试探解优劣状态由记忆元来存储.利用记忆原理的记忆增强和遗忘规律来衡量优化搜索过程中试探解的状态,并以燃料最省作为优化指标.同时采用三种不同的搜索策略,实现对试探解的随机搜索,避免陷入局部极小问题,并以此来提高搜索速度.仿真表明:卫星入轨速度偏差小于2 m/s,高度偏差小于10 m,轨道倾角偏差小于0.0001°.SRLV最终与着陆场的位置偏差小于100 m,速度偏差小于5 m/s.相较于传统的轨迹优化方法,新方法适用于复杂的轨迹/参数一体化优化问题,搜索速度快,求解精度高,有利于算法在工程实际中的应用与推广.
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