Linear fractional transformation gain-scheduling flight control preserving robust performance
Bibliographic record
Abstract
In this article, a gain-scheduling flight controller design based on a blending/interpolating methodology and an optimal linear fractional transformation (LFT)-based control technique is proposed to achieve robust performances over a wide range of aircraft operating conditions. Representing the non-linear system dynamics into an uncertain LFT system form, our gain-scheduling strategy designs a limited number of robust linear controllers covering the whole range of operating conditions. Using these fixed controllers in a robust performance control set-up, we derive a blending/interpolating scheduling controller, which achieves the desired performance for the entire flight envelope. Our approach offers the benefit of facilitating controller design and provides proofs of robust stability and performance through an uncertain LFT robust performance formulation. In addition, tools analysing the robustness of the closed-loop gain-scheduling system are also provided. Non-linear simulations based on our proposed approach for a B1 flexible aircraft for a limited flight but reasonable flight envelope show very good performance results in both time and frequency domain responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".