Spacecraft Docking Optimization: Integrating Machine Learning Approaches

This thesis endeavors to explore and substantiate the utility of a novel approach to Spacecraft Docking Optimization, which integrates Machine Learning methodologies. The research commences by validating the proposed methodology through comprehensive mathematical analysis, grounding the model in well-established mathematical theorems and principles, particularly employing Optimal Control techniques to delineate the underlying framework. Subsequent to establishing the mathematical underpinnings, the study introduces and elucidates the integration of Neural Networks within the proposed framework, highlighting their potential efficacy in addressing the complexities inherent in solving Partial Differential Equations for optimization purposes. Furthermore, the thesis progresses to conduct empirical evaluations utilizing computational simulations implemented in a Python programming environment. Through experimentation, comparisons are drawn between the conventional approach reliant on Optimal Control techniques and the Neural Network-based approach. These empirical assessments serve to ascertain the comparative effectiveness and efficiency of the two methodologies, thereby providing empirical validation of the viability of integrating Machine Learning approaches in this field, as well as many others. By demonstrating the efficacy of Neural Networks in addressing complex optimization problems, this research hopes to contribute to the broader field of optimization by offering a different approach to integrating Artificial Intelligence, specifically Machine Learning, into the optimization of dynamic models.