DANCING IN SPACE: Fuel-Optimal Formation Change Algorithms for Satellite Swarms

Abstract

This thesis presents a modular, fuel-optimal framework for autonomous reconfiguration of satellite swarms in low Earth orbit. Built using convex optimization and Clohessy-Wiltshire dynamics, the system enables agents to maneuver into desired formations while minimizing total DeltaV. It supports both centralized and event-triggered control modes, and includes logic for fault-aware role reassignment when agents fail or drift off-nominal. The architecture is designed for extensibility and validated under orbital parameters from NASA’s Starling mission, anchoring the simulations in a realistic mission context.

Beyond idealized dynamics, I extended the framework into a nonlinear regime, incorporating repulsion-based collision avoidance and full orbital propagation. Although early implementations using MATLAB’s fmincon solver failed to resolve hard-constrained formulations, a successful reconfiguration was later achieved through soft-penalized collision avoidance. This final nonlinear simulation demonstrated precise formation change under actuator and safety constraints, revealing tradeoffs between feasibility, fuel cost, and control fairness in high-dimensional swarm settings.

Across eight original simulations, I validated control strategies that are adaptive, resilient, and fuel-efficient—ranging from passive drift modeling to fault-tolerant reconfiguration, perturbed execution, and constrained nonlinear optimization. These simulations, along with the full source code and CVX routines, are publicly released on GitHub at github.com/sabrinanicacio/satellite-swarm-thesis. This thesis delivers one of the first open-source testbeds for mission-relevant satellite swarm reconfiguration using both CW-based convex planning and exploratory nonlinear control.

Together, these contributions provide a practical foundation for future work in large-scale, autonomous satellite maneuvering. By revealing the architecture-level tradeoffs between fuel use, feasibility, and safety enforcement, this project bridges a critical gap between theoretical swarm control and operational flight software.