Performance Assessment of DP Control Systems for Different Sea States
Bibliographic record
Abstract
Performances of a set of control schemes for dynamic positioning (DP) are studied in this work; DP performance is essential for future developments of autonomous shipping technology. The linear and non-linear model predictive control (MPC and NMPC), the non-linear proportional integral and derivative (NPID) control, the sliding mode control (SMC), as well as the multi-resolution PID (MRPID) control schemes, are evaluated under two different sea conditions, namely, moderate and extreme seas. Matlab/Simulink models of a full-scale ship and its corresponding scaled model are used to benchmark the efficacy of the controllers. An unscented Kalman filter (UKF) is used to estimate vessel motions and to control low frequency (LF) motions while filtering out wave frequency (WF) motions. The tuning of the controllers is also taken into consideration. Of the five controller schemes, the NMPC shows the best ability to deal with extreme disturbances efficiently. Although all of the controllers were able to maintain the ship position under moderate conditions, only the NMPC and the MRPID controllers were able to stabilize the ship under extreme sea states. Findings from this research are expected to help operators of DP systems in choosing the most effective control scheme for different sea conditions. In addition, the results are supportive of further control system development for dynamic positioning and autonomous operations of ships and offshore platforms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".