Robust Adaptive Tracking Control of Delta Wing Vortex-Coupled Roll Dynamics Subject to Delay
Bibliographic record
Abstract
In this paper a combinatory control strategy combined of a modified state feedback stabilizing controller, as the internal loop, and a robust adaptive sliding tracking controller has been proposed to be applied to the vortex-coupled roll dynamics of delta wing subject to state delay. The first subcomponent renders the closed-loop system globally practically stable. The second one is a robust adaptive sliding tracking controller which utilizes a special gaussian RBF neural network for online estimation of rolling moment coefficient as the main uncertainty of the model. To show the ability of the proposed combinatory control structure, it has been implemented to delta wing system to follow a sophisticated reference trajectory. Implementing the proposed combinatory control structure (controller with internal loop) enhanced the tracking performance in comparison to the controller without internal loop. Adding two more control inputs as a fraction of the first control input in the combinatory control structure (can be interpreted as perturbations in the vortex breakdown dynamics), also enhanced the tracking controller performance compared with the case without perturbations. Delta wing system simulation study demonstrated acceptable performance of the proposed combinatory control structure.
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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 | 0.000 | 0.001 |
| 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 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".