Model Predictive Control for Energy-Efficient Path Following Control of AUVs
Bibliographic record
Abstract
This article studies the path following (PF) control problem for an autonomous underwater vehicle. To improve the endurance, the energy management is seamlessly integrated into the PF control with the proposed multiobjective model predictive control (MOMPC) approach. In the MOMPC PF control, two control objectives, the path convergence and the energy efficiency, are considered simultaneously. The path convergence is identified as the primary task which must be guaranteed, while the energy efficiency should be improved provided the guaranteed path convergence. To accommodate the prioritized PF control objectives, the lexicographic ordering method is applied to solve the MOMPC problem. With an appropriate reference augmentation and well-designed optimization formulations, the path tracking error can be guaranteed to converge to zero, and the recursive feasibility of the MOMPC algorithm is further proved. The thrust allocation subproblem is addressed within the MOMPC PF control. Extensive simulation studies demonstrate the effectiveness of the proposed approach and show that the MOMPC presents a flexible framework to perform the energy-efficient PF control.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".