Experimental investigations of an energy-efficient dynamic positioning controller for different sea conditions
Bibliographic record
Abstract
The primary objective of a Dynamic Positioning (DP) controller is to maintain vessel position under varying environmental disturbances, while minimizing thruster usage. This work presents the development of an innovative energy-efficient DP controller, named Green NMPC (GNMPC), which minimizes thruster demand while upholding position constraints. Inspired from the structure of the economic nonlinear model predictive controller (ENMPC), GNMPC aligns with ”green” objectives and performance metrics, notably thruster energy efficiency. Extensive DP tests were conducted across a spectrum of wave conditions, including head seas, oblique angles, and large position set-point changes, to validate the efficacy of the GNMPC approach and evaluate the dynamic positioning system’s effectiveness in diverse challenging situations. The results demonstrated that the proposed controller is energy efficient compared to a benchmark NMPC and proportional–integral–derivative (PID) controller. It successfully reduced thruster demand in the sway direction compared to NMPC while preserving the vessel’s positioning objectives.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".