Initial guess generation strategies for spaceplane trajectory optimisation

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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