Online Surrogate Models for the Constrained Optimization of Interplanetary Low-Thrust Trajectories

Online Surrogate Models for the Constrained Optimization of Interplanetary Low-Thrust Trajectories

Andrade Castanheira, Francisco (TU Delft Aerospace Engineering; TU Delft Astrodynamics & Space Missions)

Cowan, K.J. (mentor)
Visser, P.N.A.M. (graduation committee)
Speretta, S. (graduation committee)

Delft University of Technology

Aerospace Engineering | Astrodynamics & Space Missions

2022-09-23

The optimization of interplanetary, low-­thrust trajectories is a computationally expensive aspect of preliminary mission design. To reduce the computational burden associated with it, surrogate models can be used as cheap approximations of the original fitness function. Training the surrogate models in a fully online manner can be done to remove the need of having previously generated datasets, which is another source of computational cost. The Sims­-Flanagan transcription is used to model an Earth-Mars transfer which is optimized through different optimization routines. The development of a C++ library with machine learning tooling was initiated, containing implementations for Generalized Regression Neural Networks (GRNNs) and Radial Basis Function Networks (RBFNs) that are used in global and local surrogates, respectively, having their hyperparameters tuned through cross-­validation. A surrogate model was constructed using Differential Evolution (DE) operators and an uncertainty-based infill criterion for the global search phase, and approximation of the derivative of the original fitness function which is provided to SNOPT (Sparse Nonlinear Optimizer), in the local search phase. An ablation study was performed to assess how each of the components of the surrogate model contribute to the results. It was verified that neither the derivative information nor the local search as a whole led to better results. The surrogate model was also outperformed by the standard optimization strategy found in literature, Monotonic Basin Hopping (MBH). Two new surrogate models incorporating ideas of this strategy were created, with one of them outperforming every other model that was tested. Despite not having performed a full study of the computational effort due to the simulations having been run in a server with a variable load, the new models present better results for similar amounts of fitness function evaluations. A Wilcoxon rank-­sum test was performed to assess whether the results have statistical significance, leading to the conclusion that a surrogate model can be used to improve the optimization of low-­thrust trajectories modeled with the Sims­-Flanagan transcription when inserted in a monotonic basin hopping optimization scheme.

Generalised Regression Neural Network
Radial Basis Function Network
GRNN
RBFN
Sims-Flanagan
SNOPT
Evolutionary Optimization
Surrogate-Assisted Optimization
Low-thrust Trajectories
Interplanetary Trajectories
Monotonic Basin Hopping
MBH
Differential Evolution
Surrogate Modelling
Space Missions
Transfer Orbits
Cross-Validation
Wilcoxon Rank-Sum Test
Machine Learning

http://resolver.tudelft.nl/uuid:f43add1f-21b9-46c1-b350-e7175e121623

2024-09-23

Student theses

master thesis

© 2022 Francisco Andrade Castanheira