Robust Consensus of Constrained AUVs With Non-Uniform Time-Varying Delays and Disturbances
Bibliographic record
Abstract
Constrained consensus formation tracking of autonomous underwater vehicle (AUV) networks is a challenging problem to solve, especially when the networks are possibly subject to nonuniform, time-varying communication delays and marine disturbances. This article presents a systematic design framework to achieve formation objectives while ensuring network stability under such uncertainties. First, a coordinate transformation is applied to the AUV kinematics to address nonholonomic constraints. A distributed consensus protocol is then used to coordinate the motion of vehicles, and utilizing the transformed kinematic model, the desired linear velocity and approach angles are determined accordingly. By employing the graph representation and Lyapunov-Krasovskii functional method, a robust stability criterion is derived in terms of linear matrix inequalities (LMIs) for a delayed network with disturbances. To improve the quality of AUV motion control, on top of the conventional backstepping controller, a sequential optimization procedure is developed for the first time, which enables optimizing the robust performance online while respecting motion constraints. Moreover, the overall stability of the resulting formation system is established. Finally, comparative simulations are carried out to verify the effectiveness and superiority of the proposed method.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".