Anti-disturbance Coordinated Path-following Control of Robotic Autonomous Surface Vehicles: Theory and Experiment
Bibliographic record
Abstract
This paper presents a guidance and control law design method for coordinated path following of networked underactuated robotic autonomous surface vehicles (ASVs) under directed communication links. Each ASV is subject to model uncertainties and environment disturbances induced by wind, waves, and ocean currents. Antidisturbance coordinated path-following controllers are designed, featured with an inner-outer loop architecture. In the outer loop, a line-of-sight guidance scheme and graph theory are employed to design guidance laws for synchronized path following. In the inner loop, an extended state observer is developed to estimate the lumped disturbances, including the model uncertainties and environmental disturbances. Based on the estimated disturbances through the extended state observer, antidisturbance kinetic control laws are designed by resorting to a dynamic surface control method. The input-to-state stability of the closed-loop system is established by cascade theory and all error signals are uniformly ultimately bounded. Finally, the results of simulation and experiment are given to illustrate the effectiveness of the proposed antidisturbance coordinated path-following controllers for underactuated ASVs.
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 | 0.001 |
| 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 | 0.000 | 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".