Lyapunov-based model predictive control for dynamic positioning of autonomous underwater vehicles
Bibliographic record
Abstract
This paper presents a novel Lyapunov-based model predictive control (LMPC) framework for the dynamic positioning (DP) control of autonomous underwater vehicles (AUVs). Due to the optimization essential, LMPC can explicitly consider the practical constraints on the real system and generate the best possible DP control. Meanwhile, taking advantage of the existing Lyapunov-based DP controller, a contraction constraint can be imposed to the formulated optimal control problem, which guarantees the closed-loop stability. In addition, the thrust allocation (TA) subproblem can be solved simultaneously with LMPC-based DP control. The most widely used proportional-integral-derivative (PID) type DP controller is investigated for the construction of the contraction constraint. Sufficient conditions that ensure the recursive feasibility hence closed-loop stability of the LMPC are derived. An arbitrarily large region of attraction can be claimed. The proposed LMPC framework serves as a bridge connecting modern optimization technique and the conventional control theory, which enables a direct integration of online optimization into control system design to improve the control performance. Simulation results on the Saab SeaEye Falcon open-frame ROV/AUV reveal the effectiveness of the proposed method.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".