Combined Time- and Frequency-Domain Aircraft System Identification Using Pareto Optimization
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".