Robust path-following control for a fully actuated marine surface vessel with composite nonlinear feedback
Bibliographic record
Abstract
This paper presents a fast and accurate robust path-following control approach for a fully actuated marine surface vessel in the presence of external disturbances. The path following is realized by simultaneously converging the yaw rate and sway velocity to their respective desired values, which are generated according to the path-following demand. An improved combined control strategy using an integral terminal sliding mode (ITSM) based composite nonlinear feedback (CNF) technique considering the external disturbances, time-varying tracking reference, input saturations and transient performance improvement is proposed in this study. The proposed ITSM-CNF combines the advantages of the CNF control in improving the transient performance and of the ITSM control in guaranteeing good robustness and finite-time convergence. A continuous and smooth sliding mode controller, based on an integral nonsingular terminal sliding surface, is added to the CNF controller to eliminate chattering. The overall stability of the closed-loop system is strictly proved based on the Lyapunov method. Simulations verify the effectiveness of the ITSM-CNF controller in improving the transient path-following performance, inhibiting overshoots, eliminating steady-state errors, rejecting external disturbances and removing chattering effects, considering input saturations, varying path curvature and finite-time convergence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".