Robust Energy-Optimal Control for 3-D Path-Following of Autonomous Underwater Vehicles Under Ocean Currents
Bibliographic record
Abstract
In this work, we propose a robust energy-optimal control that achieves 3-D path following for autonomous underwater vehicles (AUVs) in environments with ocean currents. The actual algorithm is decomposed into two elements: setpoint computation and setpoint tracking. For setpoint computation, the surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing vehicle propulsion energy considering the uncertainty set defined by the state estimate and associated uncertainty. A line-of-sight (LOS)-based guidance law, which integrates direct and indirect drift angle compensation for reduced path-following error and path-convergence time, is established to compute the yaw angle setpoints. Two setpoint-tracking model predictive controllers, minimizing a weighted sum of setpoint-tracking error and control efforts, are designed to control horizontal and vertical vehicle motion with low computational complexity. Simulation is conducted on a lawnmower-type mission under different flow conditions in the presence of measurement noises and biased ocean current estimates. The performance robustness in path following and energy saving of the proposed approach is verified through extensive numerical and theoretical analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".