Parallel Genetic Dimension Learning Particle Swarm Optimization for LEO Satellite Station Keeping with Electric Thrust

Electric propulsion for station keeping maneuvers in Low Earth Orbit (LEO) satellites can significantly reduce fuel consumption as compared to traditional chemical propulsion. However, controlling and optimizing these systems present considerable challenges. This paper proposes a novel parallel genetic dimension learning particle swarm optimization (PGDPSO) for the station keeping of LEO satellites with sequential electric propulsion. First, a high-precision orbital dynamics and station keeping model are constructed. Then, fully considering nonlinear constraints, the sequential electric thrust model with a limited number of thrust adjustments is established, which can inherently reduce maneuver frequency and mitigate thruster actuator wear. Subsequently, a fuel-optimal station keeping model is formulated and discretized using the direct method, transforming it into a large-scale nonlinear programming problem. Next, to address this, a novel PGDPSO algorithm is proposed, which significantly enhances the capability of both global exploration and local exploitation, enabling simultaneous optimization of thrust and operational time with high speed and precision. Finally, extensive simulations demonstrate that the proposed method achieves station keeping with lower fuel consumption compared to classical algorithms and exhibits a high applicability for different orbital scenarios.