Robust Composite Nonlinear Feedback Path-Following Control for Underactuated Surface Vessels With Desired-Heading Amendment
Bibliographic record
Abstract
This paper addresses the transient performance improvement 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 following due to the nonzero sideslip angle therein. Also, the disturbances in wave filed greatly affect the transient path-following control. To this end, three contributions are made in this paper: (1) an amendment to the definition of the desired heading using the sideslip-angle compensation is presented to achieve a more accurate path-following maneuver; (2) a novel disturbances observer-based composite nonlinear feedback (DO-CNF) controller is proposed to restrain system overshoots and eliminate steady-state errors while dealing with multiple oceanic disturbances with unknown bounds; and (3) the variation of the yaw-rate reference for path-following objective is handled in the controller design, which can enhance the vessel's robustness to the changing of the path curvature. Comparative simulations verify the reasonability of the desired-heading amendment and the effectiveness of the DO-CNF approach in improving the transient path-following performance of USVs while satisfying actuator saturation considering the unknown disturbances and changing reference.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".