The Application of Reinforcement Learning for Interceptor Guidance
| dc.contributor.author | Porter, Daniel Michael | en |
| dc.contributor.committeechair | Gilbert, John Nicholas | en |
| dc.contributor.committeechair | Phoenix, Austin Allen | en |
| dc.contributor.committeemember | Atkins, Ella | en |
| dc.contributor.department | Aerospace and Ocean Engineering | en |
| dc.date.accessioned | 2024-10-05T08:00:18Z | en |
| dc.date.available | 2024-10-05T08:00:18Z | en |
| dc.date.issued | 2024-10-04 | en |
| dc.description.abstract | The progression of hypersonic vehicle research and development has presented a challenge to modern missile defenses. These attack vehicles travel at speeds of Mach 5+, have low trajectories that result in late radar detections, and can be highly maneuverable. To counter this, new interceptors must be developed. This work explores using machine learning for the guidance of these interceptors through applied steering commands, with the intent to improve upon traditional guidance methods. Specifically, proximal policy optimization (PPO) was selected as the reinforcement learning algorithm due to its advanced and efficient nature, as well as its successful use in related work. A framework was developed and tuned for the interceptor guidance problem, combining the PPO algorithm with a specialized reward shaping method and tuned parameters for the engagements of interest. Low-fidelity vehicle models were used to reduce training time and narrow the scope of work towards improving the guidance algorithms. Models were trained and tested on several case studies to understand the benefits and limitations of an intelligently guided interceptor. Performance comparisons between the trained guidance models and traditional methods of guidance were made for cases with supersonic, hypersonic, weaving, and dynamically evasive attack vehicles. The models were able to perform well with initial conditions outside of their training sets, but more significant differences in the engagements needed to be included in training. The models were therefore found to be more rigid than desired, limiting their effectiveness in new engagements. Compared to the traditional methods, the PPO-guided interceptor was able to intercept the attacker faster in most cases, and had a smaller miss distance against several evasive attackers. However, the PPO-guided interceptor had a lower percent kill against nonmaneuvering attackers, and typically required larger lateral acceleration commands than traditional methods. This work acts as a strong foundation for using machine learning for guiding missile interceptors, and presents both benefits and limitations of a current implementation. Proposals for future efforts involve increasing the fidelity and complexity of the vehicles, engagements, and guidance methods. | en |
| dc.description.abstractgeneral | Hypersonic vehicles are advanced threats that are difficult to intercept due to their low trajectories, maneuverability, and high speeds. Machine learning is used to have a model learn to intelligently guide an interceptor against an attack vehicle, with the goal of protecting a target. A framework is developed and tuned to address the specifics of this problem space, using an existing advanced algorithm. Various case studies are explored, with both maneuvering and non-maneuvering attackers. The non-maneuvering cases include supersonic, constant velocity engagements with one or more targets as well as ones with hypersonic attackers and an initially stationary interceptor. The evasive methods include preplanned weaving maneuvers and dynamic evasion. The test results from these guidance models are then compared to traditional methods of guidance. Although the performance varied by case, the machine learning models were found to be fairly rigid and did not perform well in engagements that significantly differed from what they were trained on. However, some performance benefits were observed, and additional strategies may be required to increase adaptability. This work provides a foundation for proposed future work, including improving the fidelity of the models and the complexity of the engagements. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41505 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121273 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Reinforcement Learning | en |
| dc.subject | Interceptor | en |
| dc.subject | Guidance | en |
| dc.title | The Application of Reinforcement Learning for Interceptor Guidance | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Aerospace Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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