Lyapunov-based model predictive control for dynamic positioning of autonomous underwater vehicles
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it