Intelligent estimation strategies applied to a flight surface actuator
Bibliographic record
Abstract
The Kalman filter (KF) has drastically changed and formed the field of state and parameter estimation theory and has impacted a number of applications: spacecraft, GPS, fault detection and diagnosis, stock market analysis, cell phones, autonomous vehicles, to name only a few. A statistically optimal solution for known linear systems is provided by the KF, in the presence of Gaussian white noise. However, the optimality of the KF affects numerical stability and robustness. A number of linear and nonlinear forms of the KF have been introduced to overcome numerical, stability, and nonlinearity issues. In recent years, intelligent or cognitive-based KFs have been proposed. Intelligent filters generally include adaptive gains and feedback for improved estimation accuracy and robustness. These types of filters are typically more robustness to modeling uncertainties and disturbances. This paper provides a comparison of two popular KF methods: fuzzy-based and machine learning-based. These strategies are applied on a flight surface system and the estimation results are compared and discussed. Future trends in intelligent estimation theory are also considered.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".