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Optimal Spacecraft Rendezvous Using Genetic Algorithms

机译：使用遗传算法的最佳航天器交会

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The optimal rendezvous of two spacecraft are examined using a genetic algorithm. The minimum fuel solution of the optimal rendezvous contains many local optima, as well as discontinuities in the solution. These local optima and discontinuities make locating a global optimal solution difficult. Genetic algorithms are effective in solving these kinds of problems. The goal is to find the thrust time history that includes the magnitude and direction of the velocity change and the burn position, such that the boundary conditions are satisfied to an acceptable level and in a reasonable time. In addition, the number of thrust arcs and the maximum magnitude of the velocity change are regulated. This method was used on three test cases: 1) the Hohmann transfer, 2) the bielliptic transfer, and 3) rendezvous with two impulses. The results of the Hohmann and the bielliptic transfers match the analytical solutions within the resolution of the variables of the genetic algorithm. Although the result from the rendezvous with two impulses is not exact, the configuration of the trajectory is similar to the analytical solution.
机译：使用遗传算法检查了两个航天器的最佳集合点。最佳集合点的最小燃料解决方案包含许多局部最优值，以及解决方案中的不连续性。这些局部最优和不连续性使得难以找到全局最优解。遗传算法可有效解决此类问题。目的是找到包括速度变化的大小和方向以及燃烧位置的推力时间历史，以使边界条件在合理的时间内满足可接受的水平。另外，调节推力电弧的数量和速度变化的最大幅度。此方法用于三个测试用例：1）Hohmann转移，2）椭圆转移，和3）两个脉冲的集合点。 Hohmann和双椭圆转移的结果在遗传算法变量的分辨率内与解析解匹配。尽管两次脉冲交会的结果不准确，但轨迹的配置与解析解相似。

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