Repetitive Learning Observer Based Actuator Fault Detection, Isolation, and Estimation with Application to a Satellite Attitude Control System
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Bibliographic record
Abstract
An actuator fault isolation and estimation (FIE) scheme using a bank of repetitive learning observers (RLOs) for a class of discrete-time nonlinear systems is investigated in this paper. The parameters of these observers are repetitively updated using a proportional-derivative type learning algorithm at each sampling time. Based on the proposed RLOs, a group of diagnostic residuals are generated correspondingly. An actuator fault is located when only one residual goes to zero while the others do not. The parameter of the observer that locates the fault specifies the fault. Theoretically, sufficient conditions for the proposed fault detection, isolation and estimation scheme are derived. Practically, the proposed FIE scheme is applied to a satellite attitude control system, and the simulation results demonstrate its effectiveness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it