Robust Adaptive Reconfigurable Control for a Hypersonic Cruise Vehicle
Why this work is in the frame
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Bibliographic record
Abstract
The use of indirect adaptive methods to augment the nonlinear Dynamic Inversion (DI) algorithm for actuator failure reconfiguration is investigated in this paper. The ability of the adaptive reconfigurable DI algorithm to tolerate different types of actuator failure is illustrated by using a hypersonic cruise vehicle model. With an actuator failed, the on-board aerodynamic model of the DI algorithm is adapted to the vehicle performance using an online parameter identification algorithm based on a Kalman filter approach. A reconfigurable control allocation algorithm based on the weighted pseudo-inverse approach is used to redistribute the actuator commands to the remaining healthy control surfaces. To explicitly quantify the stability and performance robustness properties, the Structured Singular Value (SSV), or P-analysis, in combination with the DI controller is first formulated. Improved robustness properties of the adaptive reconfigurable controller are demonstrated through P-analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it