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arXiv · 2308.03633

Learning-based Parameter Optimization for a Class of Orbital Tracking Control Laws

Gianni Bianchini·Andrea Garulli·Antonio Giannitrapani·Mirko Leomanni·Renato Quartullo
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Abstract

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.

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Gianni Bianchini, Andrea Garulli, Antonio Giannitrapani, Mirko Leomanni, Renato Quartullo. 2023-08-07. Learning-based Parameter Optimization for a Class of Orbital Tracking Control Laws. https://arxiv.org/abs/2308.03633

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