Abstract
A system and method for a monolithic autonomous orbital vehicle enables proactive, integrated self-maintenance. A novel hybrid dual-loop control architecture provides robust stability against both predictable and unmodeled disturbances (e.g., propellant slosh). A baseline feed-forward loop cancels predictable disturbances from robotic motion. Concurrently, an adaptive feed-forward loop processes a “residual attitude error” using a prognostic-informed module, such as a Model Predictive Control (MPC) optimizer or Reinforcement Learning (RL) policy. This module receives Remaining Useful Life (RUL) estimates from a health system that detects incipient faults. The module generates a holistically optimized corrective command. A final combined command, summing the baseline and corrective commands, ensures high-precision stability during self-repair by simultaneously satisfying dynamic, health-informed constraints based on the RUL estimates.