Intelligent Control Strategies for Precise AUV Depth Regulation
Bibliographic record
Abstract
The ocean holds untapped resources and critical infrastructure, yet its exploration demands precision in navigating dynamic underwater environments. This paper advances Autonomous Underwater Vehicle depth control, enabling reliable performance in missions like offshore energy inspections and deep-sea ecological surveys. We develop and evaluate three AI-enhanced controllers, integrating neural networks and chaos game optimization for real-time adaptation to ocean currents and disturbances. Simulations testing at a 57 m target depth show that our multilayer perceptron (MLP)-based controller achieves near-zero steady-state error and reduces movement fluctuations by 31.3 % compared to fuzzy-based alternatives, ensuring smoother and more energy-efficient motion. These advancements deliver millimeter-level precision, empowering AUVs to maintain subsea cables, map hydrothermal vents, and monitor marine ecosystems with unprecedented stability, even in turbulent waters.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".