Performance Assessment of DP Control Systems for Different Sea States
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it