Trajectory Tracking Control of Autonomous Underwater Vehicles Using Improved Tube-Based Model Predictive Control Approach
Bibliographic record
Abstract
This article aims to develop a robust model predictive control (MPC) scheme for the trajectory tracking control of autonomous underwater vehicles (AUVs) subject to bounded disturbances. Based on the error dynamics model derived from the AUV dynamics and the desired trajectory, an improved tube-based MPC scheme is then developed. The tube-based MPC solves two optimal control problems, the first solves a standard problem for the nominal system which defines a reference state trajectory, and the other attempts to steer the state of the disturbed system to stay in a tube centered around the reference state trajectory thereby enabling robust control of the AUV systems. For tube-based nonlinear MPC, finding a local linear feedback to characterize the tube is challenging. To address it, we replace the local linear feedback controller with an ancillary one that incorporates the tightening constraints to ensure the disturbed system state stays in the online optimized tube. The simulation results demonstrate the effectiveness of the proposed 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".