Robust Trajectory Optimization and Control for Autonomous Small Satellite Terminal Proximity Operations under Uncertainty

This research presents a robust guidance and control architecture for SmallSat terminal proximity operations, focusing on the transition from rendezvous to final docking. As autonomous in-space servicing and assembly become mission-critical, GNC systems must maintain centimeter-level accuracy while respecting strict hardware and safety constraints in the presence of state and model uncertainties.

The methodology utilizes a numerical optimization framework based on Sequential Convex Programming to solve a multi-phase, six degree of freedom trajectory generation problem. The approach incorporates compound state-triggered constraints to manage transition logic between the fly-around and final docking corridor phases. To ensure operational reliability, the deterministic optimization is augmented with a stochastic analysis using Linear Covariance techniques. This allows for the quantification of navigation and control dispersions throughout the maneuver, enabling the formulation of chance-constrained safety boundaries that protect against collision even under Gaussian state uncertainty.

Numerical results demonstrate that the proposed framework consistently achieves convergence without the need for high-fidelity initial guesses. By parameterizing the trajectory through collocated orthogonal methods and utilizing a high-performance nonlinear programming solver, the algorithm provides computationally efficient solutions suitable for current SmallSat avionics. The analysis shows a significant reduction in propellant margin requirements by actively shaping the trajectory to minimize terminal dispersions. This work provides a scalable, non-heuristic solution for next-generation SmallSat missions requiring precision proximity maneuvers in contested or unmodeled orbital environments.