Thesis

Can spacecraft think? : Intelligent learning control onboard spacecraft

Downloadable Content

Download PDF
Creator
Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2026
Thesis identifier
  • T17977
Person Identifier (Local)
  • 201881368
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • Spacecraft guidance, navigation, and control systems need to operate under substantial uncertainties. This presents challenges when designing these control systems using conventional methods that require a certain level of knowledge of the system being controlled. Intelligent control systems have emerged as a means of addressing these challenges. These systems combine theories from automatic control, operations research, and artificial intelligence to derive controllers that can deal with different types of uncertainty. Various methods from the field of artificial intelligence can be used to develop intelligent control systems, however these are often computationally expensive which limits their applicability in spacecraft control problems. A key feature of intelligent control is the ability to adapt the control system online, which presents further difficulties when this must be done onboard a spacecraft. This thesis explores the use of reinforcement learning techniques for intelligent control applied to spacecraft powered descent. The proposed approach combines a reinforcement learning agent for handling uncertainties with conventional optimisation methods to improve the agent’s performance. In addition, the agent updates its control policy online using a novel update mechanism called Extreme Q-Learning Machine, which allows the control system to operate in a changing environment. To demonstrate the potential for this method to be implemented onboard spacecraft, results are shown from running online updates on flight suitable hardware. This work provides one possible avenue for increasing the level of intelligence of spacecraft control systems.
Advisor / supervisor
  • Riccardi, Annalisa
Resource Type
DOI

Relations

Items

Thumbnail Title Date Uploaded Visibility Actions
file details PDF of thesis T17977 2026-06-23 Public Download