Autonomous Orbital Correction for Nano Satellites Using J2 Perturbation and LSTM Networks

TL;DR

This paper introduces a hybrid control system combining J2-optimized sequences and LSTM networks to enhance orbital correction accuracy and robustness for CubeSats, effectively managing fuel use and external disturbances.

Contribution

It presents a novel integrated control strategy that combines orbital mechanics optimization with machine learning for real-time disturbance compensation in CubeSats.

Findings

Significant improvement in maneuver accuracy over traditional methods

Enhanced robustness under high uncertainty conditions

Efficient fuel consumption during orbital corrections

Abstract

CubeSats offer a cost-effective platform for various space missions, but their limited fuel capacity and susceptibility to environmental disturbances pose significant challenges for precise orbital maneuvering. This paper presents a novel control strategy that integrates a J2-optimized sequence with an LSTM-based low-level control layer to address these issues. The J2-optimized sequence leverages the Earth's oblateness to minimize fuel consumption during orbital corrections, while the LSTM network provides real-time adjustments to compensate for external disturbances and unmodeled dynamics. The LSTM network was trained on a dataset generated from simulated orbital scenarios, including factors such as atmospheric drag, solar radiation pressure, and gravitational perturbations. The proposed system was evaluated through numerical simulations, demonstrating significant improvements in maneuver accuracy and robustness compared to traditional methods. The results show that the combined system efficiently reduces miss distances, even under conditions of high uncertainty. This hybrid approach offers a powerful and adaptive solution for CubeSat missions, balancing fuel efficiency with precise orbital control.