Offset-Free Tracking Control for a Marine Autonomous Surface Ship
Bibliographic record
Abstract
Abstract The importance of autonomous surface vessels is increasing day by day because of more and more missions and safety concerns. An offset-free control system is essential for a Marine Autonomous Surface Ship (MASS) to follow its desired trajectory. The position, yaw angles, and desired velocity are the main parameters that need to be controlled precisely to keep the ship on course in the presence of environmental disturbances. In this current research, we propose an advanced control system for surface vessels that is versatile and can achieve a broad range of operational objectives while obeying safety constraints. A Nonlinear Model Predictive Control (NMPC) is the heart of our proposed system. The NMPC solves a quadratic optimization problem and dynamically calculates the control actions considering the safety constraints of the vessel. The conventional way of calculating the yaw angle and the line-of-sight guidance method for the same are compared when the trajectory tracking results are simulated in the presence of disturbances to test the performance of the controller. Such controllers are expected to provide substantial benefits by providing less operational cost, enhanced safety, and efficiency.
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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 | 0.000 |
| 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) | 0.001 | 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".