Predictive Momentum Control for High Fuel Efficiency GEO Spacecraft Stationkeeping
Bibliographic record
Abstract
To reduce stationkeeping propellant and extend orbital maneuver life, a predictive momentum adjust system has been developed for the Lockheed Martin A2100 spacecraft. This system is designed for maneuvers where high-efficiency Arcjets (AJTs) fire continuously for Delta-V, Reaction Wheel Assemblies (RWAs) are used for attitude control, and hydrazine Rocket Engine Assemblies (REAs) are pulsed for momentum control. The predictive momentum control strategy improves fuel efficiency by optimizing the use of the REAs, which have a much lower specific impulse than the AJTs. To reduce the REA firing impulse, sequences of contiguous REA pulses, or pulse bursts, are executed to drive the RWA momentum error at the end of the maneuver to zero. The final maneuver momentum error is calculated based on an estimate of the AJT disturbance impulse as well as the commanded and measured RWA momentum. By using prediction, the system eliminates REA firing in response to that portion of the momentum error that would naturally be corrected by the AJT disturbance torque alone. Innovative control logic maximizes the REA duty cycles to reduce the total number of pulses and increase the specific impulse of the firings. The system executes pulse bursts interspersed with intervals of quiescent RWA control in a way that adjusts the RWA momentum and simultaneously maintains the RWA speeds within their allowable limits. The recursive implementation provides feedback to ensure the target momentum is achieved in the presence of REA torque uncertainties and the time-varying AJT disturbance torque. The paper provides an overview of the control system logic, and includes numerical simulation results and in-orbit A2100 spacecraft flight data that illustrate the benefits of the new approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".