Adaptive SIF-KF estimation for fault detection in attitude control experiments
Bibliographic record
Abstract
An inherent property of dynamic systems with real applications is their high degree of variability, manifesting itself in ways that are often harmful to system stability and performance. External disturbances, modeling error, and faulty components must be accounted for, either in the system design, or algorithmically through estimation and control methods. In orbital satellite systems, the ability to compensate for uncertainty and detect faults is vital. Satellites are responsible for many essential operations on Earth, including GPS tracking, radio communication/broadcasting, defense, and climate monitoring. They are also expensive to design and fabricate, to deploy, and currently impossible to fix if suddenly inoperable. In being subjected to unforeseen disturbances or minor system failures, communications with Earth can cease and valuable data can be lost. Researchers have been developing robust estimation and control strategies for several decades to mitigate the effects of these failure modes. For instance, fault detection methods can be employed in satellites to detect deviations in attitude or actuator states such that error or incorrect data does not propagate further across its long life cycle. The Kalman Filter (KF) is an optimal state estimation strategy with sub-optimal nonlinear variations, commonly applied in most dynamic systems, including satellites. However, in the presence of aforementioned uncertainties, these optimal estimators tend to degrade drastically in performance, and must be replaced for more robust methods. The newly developed Sliding-Innovation Filter (SIF) is one such candidate, as it has been demonstrated to perform state estimation robustly in faulty systems. Using an in-lab Nanosatellite Attitude Control Simulator (NACS), an adaptive hybrid formulation of the SIF and EKF is applied to a satellite system to detect faults and disturbances in experiments, based on the Normalized Innovation Squares (NIS) metric. This strategy was demonstrated to improve state estimation accuracy in the presence of multiple faults, compared to conventional methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".