<p dir="ltr">The research presented in this thesis presents methods to solve multi-agent task allocation and path planning problems in the application of on-orbit servicing.</p>

Hall Effect Thrusters (HETs) are a form of electric propulsion device which uses external electrical energy to produce thrust. When compared to various other electric propulsion devices, HETs are excellent candidates for future orbit transfer and interplanetary missions due to their relatively simple configuration, moderate thrust capability, higher thrust to power ratio, and lower thruster mass to power ratio. Due to the short history of HETs, the current design process of a new HET is a largely empirical and experimental science, and this has resulted in previous designs being developed in a narrow design space based on experimental data without systematic investigations of parameter correlations. In addition, current preliminary low-thrust trajectory optimizations, due to inherent difficulties in solution procedure, often assume constant or linear performances with available power in their applications of electric thrusters. The main obstacles come from the complex physics involved in HET technology and relatively small amounts of experimental data. Although physical theories and numerical simulations can provide a valuable tool for design space exploration at the inception of a new HET design and preliminary low-thrust trajectory optimization, the complex physics makes theoretical and numerical solutions difficult to obtain. Numerical implementations have been quite extensively conducted in the last two decades. An investigation of current methodologies reveals that to date, none provide a proper methodology for a new HET design at the conceptual design stage and the coupled low-thrust trajectory optimization. Thus, in the first half of this work, an efficient, robust, and self-consistent numerical method for the analysis of HETs is developed with a new approach. The key idea is to divide the analysis region into two regions in terms of electron dynamics based on physical intuition. Intensive validations are conducted for existing HETs from 1 kW to 50 kW classes. The second half of this work aims to construct a simultaneous design optimization environment though collaboration with experts in low-thrust trajectory optimization where a new HET and associated optimal low-thrust trajectory can be designed simultaneously. A demonstration for an orbit raising mission shows that the constructed simultaneous design optimization environment can be used effectively and synergistically for space missions involving HETs. It is expected that the present work will aid and ease the current expensive experimental HET design process and reduce preliminary space mission design cycles involving HETs.

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter-dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.

<p>Autonomous guidance and control techniques for low-thrust spacecraft under multi-body dynamics via reinforcement learning</p>

<p dir="ltr"> With NASA's Artemis program and international collaborations focused on building a sustainable infrastructure for human exploration of the Moon, there is a growing demand for lunar exploration and complex spaceflight operations in cislunar space. However, designing efficient transfer trajectories between the Earth and the Moon remains complex and challenging. This investigation focuses on developing a dynamically informed framework for constructing low-energy transfers in the Earth-Moon-Sun Bicircular Restricted Four-body Problem (BCR4BP). Techniques within dynamical systems theory and numerical methods are exploited to construct transfers to various cislunar orbits. The analysis aims to contribute to a deeper understanding of the dynamical structures governing spacecraft motion. It addresses the characteristics of dynamical structures that facilitate the construction of propellant-efficient pathways between the Earth and the Moon, exploring periodic structures and energy properties from the Circular Restricted Three-body Problem (CR3BP) and BCR4BP. The investigation also focuses on constructing families of low-energy transfers by incorporating electric propulsion, i.e., low thrust, in an effort to reduce the time of flight and offer alternative transfer geometries. Additionally, the investigation introduces a process to transition solutions to the higher fidelity ephemeris force model to accurately model spacecraft motion through the Earth-Moon-Sun system. This research provides insights into constructing families of ballistic lunar transfers (BLTs) and cislunar low-energy flight paths (CLEFs), offering a foundation for future mission design and exploration of the Earth-Moon system.</p>