Tube MPC-Based Tracking Control of AUVs Using Contraction Metric
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Bibliographic record
Abstract
This paper investigates trajectory tracking of autonomous underwater vehicles (AUV) subject to thruster saturation and environmental disturbances. We propose a tube model predictive control (MPC) framework to robustly stabilize the AUV tracking error defined in the local frame. The essence of our design centers on leveraging a robust control contraction metric (RCCM) to construct a disturbance invariant set, ensuring bounded deviation between the actual and nominal system states under the RCCM-based feedback control law. Subsequently, an outer approximation of this RCCM-based invariant set is developed to design the tube cross-section and tighten the input constraint. The resulting RCCM-based tube MPC (RCCM-MPC) scheme is independent of the spatially varying metric, enhancing the computational efficiency of the proposed scheme. Then we establish sufficient conditions for ensuring the recursive feasibility of the proposed RCCM-MPC scheme and stability of the closed-loop tracking error dynamics. Simulation results demonstrate the effectiveness of the proposed RCCM-MPC approach.
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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