Combined Time- and Frequency-Domain Aircraft System Identification Using Pareto Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Aircraft system identification can either occur in the time-or frequency-domain with each approach having inherent advantages and disadvantages. For example, time-domain modelling generates superior time history matches and has a superior ability to achieve a trim solution. However, time-domain models do not provide a high degree of insight to the frequency responses of the system, which is important for control law development and for matching handling qualities for pilot-in-the-loop simulation — this is a strength of the frequency-domain approach. This paper utilises a Pareto optimization procedure to combine both the time- and frequency-domain approaches and exploit the strengths of both methods. Pareto fronts are generated for the system identification of a 6 degree-of-freedom forward flight model at 90 kts of the National Research Council of Canada’s Bell 412 helicopter. The generated Pareto fronts showed the necessity of balancing the time- and frequency-domain matches whereby moving from the compromise solution to either the isolated time- or frequency-domain solutions resulted in a small improvement in one while the other suffered relatively more. Accordingly, the multi-objective solution using Pareto optimization capitalized on the strengths of both approaches and avoided an overspecialized solution in either of the domains.
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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