Path-following control of an AUV using multi-objective model predictive control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The path-following (PF) problem of an autonomous underwater vehicle (AUV) is studied, in which the speed profile of the vehicle is taken into consideration as a secondary task. A multi-objective model predictive control (MO-MPC) framework is developed attempting to accommodate the prioritized tasks in PF. To solve the MO-MPC problem, we adopt the weighted sum method with the introduction of a logistic function that automatically selects the appropriate weights for each objective function. Pontryagin minimum principle (PMP) is subsequently applied for the implementation. Simulations using identified hydrodynamic coefficients of the Saab SeaEye Falcon open-frame ROV/AUV are carried out, which validates the effectiveness of the proposed MO-MPC path-following control.
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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 | 0.001 |
| 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