Hybrid adaptive control of autonomous underwater vehicle
Why this work is in the frame
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Bibliographic record
Abstract
Hybrid adaptive control of autonomous underwater vehicle (AUV) is investigated. Dynamics of AUV vary by change in operating conditions and even theoretically or experimentally driven dynamical coefficients reflect an approximate to the exact ones. Adaptive control technique is employed to handle the uncertainty problems in the system dynamics. In the applied hybrid adaptive control, the system is simulated in a continuous domain while the control and identification sections are discrete. The discrete model and position of zeros of sampled data unstable system are addressed. Convergence rate of parameter estimation is crucial in the stability of closed loop system particularly when open loop unstable system passes its initial states or is entangled by radical changes in the dynamics. Adaptive normalization is suggested which improves the rate of convergence and conserves stability. The results of modified direct, indirect and linear quadratic Gaussian (LQG) adaptive control are presented.
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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) | 0.000 | 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) | 0.001 | 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