Physics-Informed Neural Networks for Designing Spacecraft Trajectories

This thesis proposes an unsupervised Physics-Informed Neural Network (PINN) for solving optimal control problems with the direct method to design and optimize transfer trajectories. The network adheres analytically to boundary conditions and includes the objective fitness as regularization in its loss function. A test scenario of a planar Earth...

Cluster-based search space pruning in single leg low-thrust trajectory optimisation problems

Many contemporary interplanetary missions use efficient low-thrust engines to reach the far corners of our Solar System. Their trajectories, however, have proven to be complicated to optimise due to the non-impulsive manoeuvres involved in low-thrust spaceflight. Even though shaping methods have been used extensively to reduce the computational...

Global Optimization of Low-Thrust Interplanetary Trajectories Using a Machine Learning Surrogate

In this thesis, a new method to approximate the cost function of Low-Thrust, Multiple-Gravity-Assist interplanetary trajectories using a Machine Learning surrogate is proposed. This method speeds up the optimization process without fine tuning of the surrogate parameters for every individual case. The computational cost of obtaining training...

Investigating the use of neural network surrogate models in the evolutionary optimization of interplanetary low-thrust trajectories

Building on recent advances in the fields of low-thrust trajectory optimization based on shaping methods, Artificial Neural Networks, and surrogate models in Evolutionary Algorithms, an investigation into a novel optimization routine is conducted. A flexible Python tool to evaluate linked trajectories in a two-body model based on the hodographic...