Identification of a MIMO state space model of an F/A-18 aircraft using a subspace method
Bibliographic record
Abstract
Abstract The aim of this paper is to determine the mathematical relationship (model) between control deflections and structural deflections of the F/A-18 modified aircraft in the active aeroelastic wing technology program. Five sets of signals from flight flutter tests corresponding to the excited sources were measured by NASA Dryden Flight Research Center. These excitation inputs are: differential ailerons, collective ailerons, collective stabilisers, differential stabilisers, and rudders. The signals to be used by the model are of two types: control deflection time histories and corresponding structural deflections on the wing and trailing-edge flaps. We choose to use the subspace identification method based on reconstructing the observability matrix in order to identify the nonlinear multi-input, linear-in-the-states, multi-output system. We identify models (input/output characteristics) by applying this method for a number of sixteen flight conditions for which the Mach number varies from 0·85 to 1·30 and the altitudes vary from 5,000ft to 25,000ft. Very good results are obtained with a fit between the estimated and the measured signals and a correlation coefficient higher than 90%.
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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 | 0.000 |
| 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) | 0.001 | 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".