Initial guess generation strategies for spaceplane trajectory optimisation

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Toso, Federico and Maddock, Christie (2017) Initial guess generation strategies for spaceplane trajectory optimisation. Transactions of the Japan Society for Aeronautical and Space Sciences. ISSN 0549-3811 (In Press)

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Abstract

Trajectory optimisation for spaceplanes is a highly complex problem due to the nonlinearity of the dynamics, long integration times, high energy environment and a broad spectrum of different flight conditions from sea level to space. In this paper, strategies are analysed for the fast and autonomous generation of initial guesses for a gradient-based solver for the ascent trajectory of a multi-stage reusable spaceplane launch vehicle. Different multi-start strategies are used to generate an archive of solutions with the performances analysed for computational run time, convergence rate and violation level of the constraints. A focus is also put on methods that reduce the dependency on the expertise of the user to produce a problem-specific first guess. Different approaches are analysed that introduce a weighting of the constraints relative to the objective function, add low levels of white noise, and conduct an initial sorting using larger integration time steps. A promising compromise between convergence rate, run time and automation is achieved with the introduction of low level white noise to unconverged solutions from a population of first guess solutions created using Latin Hypercube Sampling.

ORCID iDs

Toso, Federico ORCID logoORCID: https://orcid.org/0000-0002-9698-5523 and Maddock, Christie ORCID logoORCID: https://orcid.org/0000-0003-1079-4863;
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