NMPC Design for AUV Dynamic Positioning Control with Incremental Input Constraints
Bibliographic record
Abstract
This paper presents a novel nonlinear model pre-dictive control (NMPC) method for the dynamic positioning (DP) problem of general autonomous underwater vehicle (AUV) systems. In addition to common bound constraints on the control input signal, the input increments are particularly considered for smooth actuation. With a Lyapunov-based auxiliary DP con-troller, we show that the input constraints and incremental input constraints can be fully respected. The widely used proportional-integral-derivative (PID) type control law is exploited as the auxiliary DP controller, and then conditions that guarantees the recursive feasibility and closed-loop stability are derived. The feasibility and stability do not rely on the optimality of the control solution, but the control performance improves as the solution approaches to the (local) optimum. Hence, the computational complexity and control performance can be well balanced by the user specified optimizer parameters. Simulation results reveal the effectiveness and advantages of the proposed NMPC 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 | 0.000 |
| 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".