Control-Oriented Modeling and Cascade Control of Container Ships
Bibliographic record
Abstract
The development of autonomous ship motion control is gaining importance in the maritime transportation industry as autonomous ships can improve safety, efficiency, capacity, and environmental impact while reducing labor costs. However, most research on this topic has focused on nonlinear ship models coupled with complex control system designs, which are not feasible for some scenarios such as executing turning maneuver in real-time application for typical mass-produced cargo ships due to needs for high computational capability. To address this issue, this paper presents a practical control-oriented ship model using a superposed nonlinear propeller model on a linear ship motion dynamics. The developed model is validated using standard turning circle tests and a cascaded control algorithm is designed for waypoint tracking. The algorithm is tested with different scenarios to evaluate its ability to move the ship along prescribed trajectories, and simulation results show successful performance of the controller. Overall, this approach provides a reliable and practical solution for controlling autonomous ships in real-time applications.
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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".