Optimal gain-scheduled flight control system design using a new fuzzy clustering algorithm
Bibliographic record
Abstract
This article presents an analytical framework for the design of autopilots using fuzzy systems. A new fuzzy clustering method is presented in this article, which is used to model a non-linear flight vehicle using a set of linear models. It is shown that the proposed fuzzy clustering technique produces lower estimation errors compared with other fuzzy clustering techniques; it means that the modelling error using the technique introduced is lower than other clustering methods. The membership functions and rule sets, which are obtained by fuzzy clustering, are then applied to a set of linear time-invariant optimal state-feedback controllers, obtained for each rule, towards extraction of the global non-linear controller matching closely with the dynamic properties and changes in the plant. The stability of the fuzzy model and the fuzzy system is established by the Lyapunov-based linear matrix inequality analysis. Simulation studies are reported to demonstrate the merits of the fuzzy set-based modelling and control approach in handling the demanding non-linear modelling and control task.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".