Intelligent Perturbation-Driven control methodology for Low-Thrust satellite constellation systems

This study proposes a closed-form dynamical modeling and intelligent control methodology for low-thrust RGT-Walker satellite constellations. By analytically incorporating the first-order secular J 2 perturbations, the presented dynamic framework enables unified ground track maintenance and precise constellation-wide control based on the reference trajectory. The hybrid Intelligent Perturbation-Driven (IPD) Control system, combining Proportional-Derivative (PD) feedback and real-time environmental disturbance learning via Long Short-Term Memory (LSTM) networks, maximizes adaptive thrust scheduling and fuel efficiency. Numerical simulations under both nominal and worst-case disturbance scenarios validate stable orbit maintenance, 50 % reduction in propellant consumption, and a twofold extension of mission lifetime. This methodology offers a robust and scalable academic foundation for advancing the autonomy, efficiency, and precision of next-generation low-thrust electric propulsion satellite constellation operations.

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Publication Details

Journal
Ain Shams Engineering Journal
Published
2026-06-16
DOI
https://doi.org/10.1016/j.asej.2026.104282
Primary Topic
Spacecraft Dynamics and Control
Type
article
Field-Weighted Citation Impact
0.00

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article

Intelligent Perturbation-Driven control methodology for Low-Thrust satellite constellation systems

Soung Sub Lee
Ain Shams Engineering Journal
Spacecraft Dynamics and Control
article

Intelligent Perturbation-Driven control methodology for Low-Thrust satellite constellation systems

Soung Sub Lee
article en

Abstract

This study proposes a closed-form dynamical modeling and intelligent control methodology for low-thrust RGT-Walker satellite constellations. By analytically incorporating the first-order secular J 2 perturbations, the presented dynamic framework enables unified ground track maintenance and precise constellation-wide control based on the reference trajectory. The hybrid Intelligent Perturbation-Driven (IPD) Control system, combining Proportional-Derivative (PD) feedback and real-time environmental disturbance learning via Long Short-Term Memory (LSTM) networks, maximizes adaptive thrust scheduling and fuel efficiency. Numerical simulations under both nominal and worst-case disturbance scenarios validate stable orbit maintenance, 50 % reduction in propellant consumption, and a twofold extension of mission lifetime. This methodology offers a robust and scalable academic foundation for advancing the autonomy, efficiency, and precision of next-generation low-thrust electric propulsion satellite constellation operations.

Ain Shams Engineering JournalVol. 17(8)
Sejong University (KR)
Sejong University
Affordable and clean energy
Openalex Percentile: Top 3%
Spacecraft Dynamics and Control
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