Initial guess generation strategies for spaceplane trajectory optimisation

Research output: Contribution to journalConference Contribution

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.

Fingerprint

trajectory optimization
Trajectory Optimization
Guess
trajectory
Trajectories
White noise
Time Integration
white noise
Convergence Rate
ascent trajectories
Latin Hypercube Sampling
Multistart
flight conditions
Ascent
launch vehicles
Launch vehicles
Sea level
automation
Expertise
classifying

Keywords

  • first guess
  • trajectory optimisation
  • spaceplane
  • access to space

Cite this

@article{4c95fa775fea432f8512ca1263205058,
title = "Initial guess generation strategies for spaceplane trajectory optimisation",
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.",
keywords = "first guess, trajectory optimisation, spaceplane, access to space",
author = "Federico Toso and Christie Maddock",
year = "2017",
month = "1",
day = "10",
language = "English",
journal = "Transactions of the Japan Society for Aeronautical and Space Sciences",
issn = "0549-3811",
publisher = "Japan Society for Aeronautical and Space Sciences",

}

TY - JOUR

T1 - Initial guess generation strategies for spaceplane trajectory optimisation

AU - Toso, Federico

AU - Maddock, Christie

PY - 2017/1/10

Y1 - 2017/1/10

N2 - 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.

AB - 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.

KW - first guess

KW - trajectory optimisation

KW - spaceplane

KW - access to space

UR - http://www.ists.or.jp/

UR - https://www.jstage.jst.go.jp/browse/tjsass

M3 - Conference Contribution

JO - Transactions of the Japan Society for Aeronautical and Space Sciences

T2 - Transactions of the Japan Society for Aeronautical and Space Sciences

JF - Transactions of the Japan Society for Aeronautical and Space Sciences

SN - 0549-3811

ER -