Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and Attitude Control

TL;DR

This paper introduces a learning-based method to compute power-optimal currents for magnetorquers, enhancing multi-satellite formation and attitude control efficiency through convex optimization and neural network approximation.

Contribution

It develops a novel, continuous, power-optimal current solution for magnetorquers using sequential convex programming and neural networks, validated by simulations and experiments.

Findings

Demonstrated effective power optimization in satellite attitude control.

Validated the approach through numerical simulations.

Confirmed practical applicability with experimental trials.

Abstract

This paper presents a learning-based current calculation model to achieve power-optimal magnetic-field interaction for multi-agent formation and attitude control. In aerospace engineering, electromagnetic coils are referred to as magnetorquer (MTQ) coils and used as satellite attitude actuators in Earth's orbit and for long-term formation and attitude control. This study derives a unique, continuous, and power-optimal current solution via sequential convex programming and approximates it using a multilayer perceptron model. The effectiveness of our strategy was demonstrated through numerical simulations and experimental trials on the formation and attitude control.