Robust Composite Nonlinear Feedback Control for Path Following of Underactuated Surface Vessels With Desired-Heading Amendment
Bibliographic record
Abstract
This paper studies the transient performance improvement problem for path following control of underactuated surface vessels (USVs) in the presence of oceanic disturbances. The traditional practice that chooses the tangent direction of the desired path as the desired heading may deteriorate the tracking performance in the curve-path following. That is because the sideslip angle is not zero in turnings, which unavoidably makes the lateral offset hard to converge to zero. Also, the disturbances in wave filed greatly affect the transient control of the path following errors. To this end, this paper makes two contributions: 1) An amendment on the choice of the desired heading is presented to consider the sideslip angle in turnings and then achieve a more accurate path-following maneuver; 2) A novel robust composite nonlinear feedback (CNF) technique is proposed based on a multiple-disturbances observer to improve the transient performance for path following control in seaway environment considering the input saturation. Comparative simulations verify the reasonability of the amendment on the desired heading direction and the effectiveness of the CNF approach in improving the transient performance for the path following control of USVs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".