Machine learning-aided trajectory optimization and tour design

This dissertation investigates machine learning (ML)-aided methods for preliminary space mission design, with a focus on trajectory optimization and tour design for interplanetary missions involving multiple gravity assists. In early-stage trajectory analysis, the repeated solution of Lambert’s problem often becomes a major computational bottleneck. Meanwhile, the number of possible flyby sequences grows combinatorially as the number of flybys increases, and each sequence is associated with a distinct cost function topology, making optimization and trade-space exploration especially challenging. To address these issues, this dissertation develops and evaluates several ML-aided frameworks for rapid transfer approximation, flyby-sequence optimization, and reinforcement-learning-based trajectory optimization. First, a neural-network-based Lambert approximator is developed to provide rapid estimates of transfer legs needed for large-scale trajectory searches while maintaining sufficient accuracy for preliminary mission design. Second, the neural network model for Lambert’s problem is integrated into the dynamic-size multiple-populations genetic algorithm (DSMPGA), and a vectorized implementation is adopted to enhance computational speed. This approach handles variable-length gravity-assist trajectories and generates both optimal and suboptimal mission candidates. Third, reinforcement learning is investigated as a sequential decision-making framework for multiple-gravity-assist trajectory optimization. In addition, deep learning is investigated for the placement of deep-space maneuvers, and the Extreme Theory of Functional Connections (X-TFC) is applied to orbital transfer problems under non-Keplerian dynamics, including J2-perturbed two-body motion and the circular restricted three-body problem. Overall, the results demonstrate that ML can accelerate preliminary trajectory design, improve the exploration of large and complex search spaces, and provide a promising foundation for future ML-aided mission design frameworks in space exploration.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

dissertation

Discipline thesis:degree_discipline

Aerospace engineering

Grantor

Iowa State University - Thesis & Dissertation

Year dc:date.issued

2026

Author dc:creator

• Choi, Sungmoon

Advisor dc:contributor.advisor

• Abdelkhalik, Ossama

• Aerospace engineering

Language dc:language.iso

en_US

Repository record dc:identifier.uri

https://dr.lib.iastate.edu/handle/20.500.12876/106702

OAI identifier oai:identifier

oai:dr.lib.iastate.edu:20.500.12876/106702