Gain-scheduled flight control law design using a new fuzzy clustering technique
Bibliographic record
Abstract
In this paper a gain scheduling methodology is proposed, which exploits a fuzzy modeling technology based on Quasi-Linear Parameter Varying (QLPV) description of a nonlinear model of a missile, to resolve two main deficiencies of classical gain scheduling approaches (restriction to near equilibrium operation and lack of a satisfactory interpolation mechanism). A new clustering approach (designated as fuzzy clustering based classification trees: FC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> T) is employed in fuzzy modeling. It is shown that employment of FC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> T in the development of fuzzy model for the system produces a lower estimation error than Gustafson-Kessel fuzzy clustering and a tree partitioning algorithm, while it does not confront by randomly initialization problem. Merits of the proposed fuzzy model-based gain scheduling technique are demonstrated in a demanding application. Simulation studies are reported to demonstrate the stability, the performance and the robustness of the designed fuzzy controller.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".