Modified C/GMRES Algorithm for Fast Nonlinear Model Predictive Tracking Control of AUVs
Bibliographic record
Abstract
This brief presents a nonlinear model predictive control (NMPC) method for the trajectory tracking problem of an autonomous underwater vehicle (AUV). By augmenting the desired output trajectory to a reference dynamical system, the tracking task can fit into the standard NMPC framework, which effectively avoids possible numerical difficulties in the following fast NMPC implementation. To relieve the conflict between short sampling period and high demand of online calculation, Ohtsuka's continuation/generalized minimal residual (C/GMRES) algorithm is investigated. In order to handle the realistic constraints on the AUV thrusters, we incorporate the log barrier functions into the cost function and modify the C/GMRES algorithm. Several different reference trajectories are tested using the identified dynamic model of the Saab SeaEye Falcon open-frame ROV/AUV, which demonstrate the effectiveness and efficiency of the proposed fast algorithm for the AUV tracking control.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".