This paper presents a machine learning approach for tuning the parameters of a family of stabilizing controllers for orbital tracking. An augmented random search algorithm is deployed, which aims at minimizing a cost function combining convergence time and fuel consumption. The main feature of the proposed learning strategy is that closed-loop stability is always guaranteed during the exploration of the parameter space, {a property that allows one to streamline the training process by restricting the search domain to well-behaved control policies.} The proposed approach is tested on two case studies: an orbital transfer and a rendezvous and docking mission. It is shown that in both cases the learned control parameters lead to a significant improvement of the considered performance measure.
@article{arxiv.2308.03633,
title = {Learning-based Parameter Optimization for a Class of Orbital Tracking Control Laws},
author = {Gianni Bianchini and Andrea Garulli and Antonio Giannitrapani and Mirko Leomanni and Renato Quartullo},
journal= {arXiv preprint arXiv:2308.03633},
year = {2023}
}