Imitation Learning for Satellite Attitude Control under Unknown Perturbations

TL;DR

This paper introduces a reinforcement learning-based satellite attitude control framework that combines SAC and GAIL to enhance robustness and reduce training costs under unknown perturbations.

Contribution

It develops a SAC-based expert controller for resilient satellite attitude management and employs GAIL to efficiently imitate expert trajectories, improving generalization and reducing sample complexity.

Findings

SAC expert outperforms previous controllers under various perturbations.

GAIL successfully imitates SAC expert trajectories, reducing training data needs.

The integrated approach enhances robustness and autonomy of satellite control systems.

Abstract

This paper presents a novel satellite attitude control framework that integrates Soft Actor-Critic (SAC) reinforcement learning with Generative Adversarial Imitation Learning (GAIL) to achieve robust performance under various unknown perturbations. Traditional control techniques often rely on precise system models and are sensitive to parameter uncertainties and external perturbations. To overcome these limitations, we first develop a SAC-based expert controller that demonstrates improved resilience against actuator failures, sensor noise, and attitude misalignments, outperforming our previous results in several challenging scenarios. We then use GAIL to train a learner policy that imitates the expert's trajectories, thereby reducing training costs and improving generalization through expert demonstrations. Preliminary experiments under single and combined perturbations show that the SAC expert can rotate the antenna to a specified direction and keep the antenna orientation reliably stable in most of the listed perturbations. Additionally, the GAIL learner can imitate most of the features from the trajectories generated by the SAC expert. Comparative evaluations and ablation studies confirm the effectiveness of the SAC algorithm and reward shaping. The integration of GAIL further reduces sample complexity and demonstrates promising imitation capabilities, paving the way for more intelligent and autonomous spacecraft control systems.