Neural adaptive observer based fault detection and identification for satellite attitude control systems
Why this work is in the frame
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Bibliographic record
Abstract
A neural adaptive observer (NAO) based fault detection and identification (FDI) strategy for a class of nonlinear systems is presented in this paper. The observer input is designed in a structure similar to feedback neural networks. The parameters in the NAO input are updated by using the extended Kalman filter (EKF) algorithm. The convergence of the learning process is analyzed in terms of a quadratic Lyapunov function. Moreover, stability of the observer input and the NAO-based system are investigated respectively. Finally, the proposed FDI strategy is applied to a micro-satellite attitude control system. Several simulation results demonstrate that the NAO based FDI method can detect and specify both abrupt and incipient faults with satisfactory performance.
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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 | 0.000 | 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 it