Low-Thrust Under-Actuated Satellite Formation Guidance and Control Strategies

TL;DR

This paper develops autonomous guidance and control strategies for low-thrust, under-actuated satellite formations, focusing on trajectory optimization under physical constraints, with centralized and distributed approaches for close-range reconfiguration.

Contribution

It introduces a novel framework combining centralized and distributed guidance strategies for low-thrust satellite formations, including feasibility softening and Model Predictive Control architectures.

Findings

Distributed approach offers scalability with acceptable sub-optimality.

Model Predictive Control schemes improve control accuracy and fuel efficiency.

Strategies are validated on Earth observation mission scenarios.

Abstract

This study presents autonomous guidance and control strategies for the purpose of reconfiguring close-range multi-satellite formations. The formation under consideration includes $N$ under-actuated deputy satellites and an uncontrolled virtual or physical chief spacecraft. The guidance problem is formulated as a trajectory optimization problem that incorporates typical dynamical and physical constraints, alongside a minimum acceleration threshold. This latter constraint arises from the physical limitations of the adopted low-thrust technology, which is commonly employed for precise, close-range relative orbital maneuvers. The guidance and control problem is addressed in two frameworks: centralized and distributed. The centralized approach provides a fuel-optimal solution, but it is practical only for formations with a small number of deputies. The distributed approach is more scalable but yields sub-optimal solutions. In the centralized framework, the chief is a physical satellite responsible for all calculations, while in the distributed framework, the chief is treated as a virtual point mass orbiting the Earth, and each deputy performs its own guidance and control calculations onboard. The study emphasizes the spaceborne implementation of the closed-loop control system, aiming for a reliable and automated solution to the optimal control problem. To this end, the risk of infeasibility is mitigated through first identifying the constraints that pose a potential threat of infeasibility, then properly softening them. Two Model Predictive Control architectures are implemented and compared, namely, a shrinking-horizon and a fixed-horizon schemes. Performances, in terms of fuel expenditure and achieved control accuracy, are analyzed on typical close-range reconfigurations requested by Earth observation missions and are compared against different implementations proposed in the literature.