Spaceplane trajectory optimisation with evolutionary-based initialisation

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.