Adaptive control of hypersonic vehicles using intelligent allocation
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an intelligent allocation-based adaptive controller for the longitudinal motion of air-breathing hypersonic vehicles (AHVs). A control-allocation (CA) module is developed to deal with composite actuator servo constraints that have usually been neglected in existing AHV control works. This CA process is realized with the help of the recent deep deterministic policy gradient algorithm without involving any online optimization. In the followed adaptive controller design, several auxiliary signals are constructed to compensate for the possible CA error, while adaptive super-twist differentiators are employed to fast estimate the lumped effect of uncertain aerodynamic coefficients and unknown external disturbances. As a result, the adaptive control algorithm only needs three parameter updating laws, whose dimension is much lower than traditional adaptive control strategies for AHVs. Simulations are provided to verify the proposed intelligent allocation-based adaptive controller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it