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

TL;DR

This paper introduces a machine learning method using augmented random search to optimize parameters of orbital control laws, ensuring stability and improving performance in transfer and docking missions.

Contribution

It presents a novel learning-based parameter tuning approach that guarantees stability during training for orbital control laws.

Findings

Significant performance improvements in orbital transfer and rendezvous tasks.

Stable training process due to guaranteed closed-loop stability.

Effective parameter optimization with minimal fuel consumption.

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.