Alternative trajectory-tracking control approach for marine surface vessels with experimental verification
Bibliographic record
Abstract
SUMMARY Experiments with a nonlinear trajectory-tracking controller for marine unmanned surface vessels are reported. The tracking controller is designed using a nonlinear robust model-based sliding mode approach. The marine vehicles can track arbitrary desired trajectories that are defined in Cartesian coordinate as continuous functions of time. The planar dynamic model used for the controller design consists of 3 degrees of freedom (DOFs) of surge, sway, and yaw. The vessel only has two actuators, so the vessel is underactuated. Therefore, only two outputs, which are functions of the 3-DOF, can be controlled. The Cartesian position of a control point on the vessel is defined as the output. The orientation dynamics is not directly controlled. It has been previously shown that the orientation dynamics, as the internal dynamics of this underactuated system, is stable. The result of field experiments show the effectiveness of tracking control laws in the presence of parameter uncertainty and disturbance. The experiments were performed in a large outdoor pond using a small test boat. This paper reports the first theoretical development and experimental verification of the proposed model-based nonlinear trajectory-tracking controller.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".