Hybrid Orbital Vehicle Control for Self-Repair Disturbance Rejection
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Solution Overview
Problem
Existing monolithic orbital vehicles face significant challenges in maintaining high-precision attitude stability during robotic self-repair due to both predictable and unpredictable disturbances, such as propellant slosh and structural flexion, which conventional feedback-only and static feed-forward control systems are inadequate to address.
Innovation Solution
A hybrid dual-loop control architecture that combines a baseline kinodynamic model with an adaptive disturbance and health management module to proactively cancel all disturbances, using Prognostic-Informed Model Predictive Control (MPC) or Deep Reinforcement Learning (RL) to optimize real-time corrective commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional feedback-only attitude control system (such as a PID controller) is used, then the system structure is simple, but it can only react to measured attitude error after disturbance has occurred, leading to unacceptable pointing errors for precision tasks
Solution Approach 1:
The patent implements a feed-forward control component that proactively compensates for predicted disturbances before they affect the spacecraft attitude. The controller uses a predictive model to calculate expected disturbance torques from robotic manipulator operations and applies compensating torques in advance, rather than waiting for attitude errors to occur. This preliminary action enables high-precision attitude control during robotic self-repair operations without requiring excessively complex feedback-only systems.
2Adaptability or versatility
If a static feed-forward control approach is used to cancel primary disturbances from manipulator motion, then the known kinodynamic disturbances are compensated, but it fails to address unmodeled and time-varying disturbances such as propellant slosh and thermal deformation
Solution Approach 1:
The patent transitions from static feed-forward control to a dynamic adaptive control architecture. The controller continuously updates its disturbance compensation commands based on real-time sensor measurements of attitude errors and rates. This dynamic adaptation allows the system to handle unmodeled and time-varying disturbances such as propellant slosh, thermal deformation, and center of mass shifts that occur during robotic manipulation and component replacement operations.
Solution Approach 2:
The patent implements a hybrid control architecture that combines feed-forward prediction with real-time feedback from attitude sensors. The feedback component monitors actual attitude deviations and uses these measurements to adjust the feed-forward compensation commands, creating a closed-loop adaptive system. This feedback mechanism enables the controller to compensate for unmodeled disturbances and maintain precision despite the increased complexity of the control architecture.
3Reliability
If a monolithic vehicle architecture is used to integrate self-repair capabilities, then the risks of inter-spacecraft operations are eliminated, but significant and complex disturbance forces and torques are induced on the spacecraft bus during robotic manipulation
Solution Approach 1:
The patent applies preliminary anti-action by using the feed-forward control component to predict and counteract disturbance torques generated by robotic manipulator operations before they affect spacecraft attitude. The controller uses a predictive model of manipulator dynamics to calculate compensating torques and applies them proactively, preventing the harmful effects of manipulation-induced disturbances on attitude stability while maintaining the reliable monolithic architecture.
Data Source
AI summary
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.