Spaceplane trajectory optimisation with evolutionary-based initialisation

C. Maddock, E. Minisci
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引用次数: 3

Abstract

In this paper, an evolutionary-based initialisation method is proposed based on Adaptive Inflationary Differential Evolution algorithm, which is used in conjunction with a deterministic local optimisation algorithm to efficiently identify clusters of optimal solutions. The approach is applied to an ascent trajectory for a single stage to orbit spaceplane, employing a rocket-based combine cycle propulsion system. The problem is decomposed first into flight phases, based on user defined criteria such as a propulsion cycle change translating to different mathematical system models, and subsequently transcribed into a multi-shooting NLP problem. Examining the results based on 10 independent runs of the approach, it can be seen that in all cases the method converges to clusters of feasible solutions. In 40% of the cases, the AIDEA-based initialisation found a better solution compared to a heuristic approach using constant control for each phase with a single shooting transcription (representing an expert user). The problem was run using randomly generated control laws, only 2/20 cases converged, both times with a less optimal solution compared to the baseline heuristic approach and AIDEA.
基于演化初始化的空天飞机轨迹优化
本文在自适应膨胀差分进化算法的基础上,提出了一种基于进化的初始化方法,并将其与确定性局部优化算法相结合,有效地识别出最优解簇。该方法应用于单级入轨航天飞机的上升轨迹,采用火箭基联合循环推进系统。该问题首先分解为飞行阶段,基于用户定义的标准,如推进周期变化转换为不同的数学系统模型,然后转录为多射击NLP问题。通过对该方法进行10次独立运行的结果检验,可以看出,在所有情况下,该方法都收敛于可行解的聚类。在40%的情况下,与启发式方法相比,基于aidea的初始化找到了更好的解决方案,启发式方法使用单个射击转录对每个阶段进行恒定控制(代表专家用户)。该问题使用随机生成的控制律运行,只有2/20的情况收敛,与基线启发式方法和AIDEA相比,这两次的最优解都较差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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