Nonlinear model predictive control for trajectory tracking of an AUV: A distributed implementation
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the nonlinear model predictive control (NMPC) method for the trajectory tracking application of an autonomous underwater vehicle (AUV). To formulate the tracking control problem into the standard MPC scheme, the desired spatial reference trajectory is augmented according to the kinematic property of the AUV motion, which facilitates the following distributed model predictive control (DMPC) implementation. Considering that the computational complexity of the nonlinear programming (NLP) problem associated to the NMPC tracking control could be prohibitively high, the DMPC implementation is proposed attempting to alleviate the computational burden. The six degree of freedom (DOF) AUV model is then decomposed into three slightly coupled subsystems, and the DMPC subproblems are well defined with the original cost function broken down appropriately. Warm start strategy is adopted to enhance the control performance. Simulation studies are carried out, which verifies 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 | 0.000 |
| Open science | 0.000 | 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