Data-Driven Nonlinear Model Predictive Control of an Autonomous Uncrewed Surface Vessel
Bibliographic record
Abstract
Small surface vessels are a vital part of many Canadian and global operations including transportation, environmental surveying, and emergency response. Increasing the autonomy of these vessels could provide benefits by reducing on-board labour requirements, reducing travel time, and increasing fuel efficiency. With traditional control techniques, models for Uncrewed Surface Vessels (USVs) do not take into account evolving disturbances such as the wind, waves, and water current due to the complexity of these environmental forces. However, knowledge of these disturbances can improve controller performance and safety strategies for control. The Sparse Identification of Non-linear Dynamics (SINDy) is a data-driven model identification technique that provides human-interpretable system models in short training times using low amounts of training data. In this work, SINDy was used to create a USV model from same-day data, capturing up to date changes in the vehicle performance and environmental conditions. That model was then combined with real-time Nonlinear Model Predictive Control (NMPC) to create a data-driven control strategy for USV path following. Field experiments in the Great Cataraqui River (Kingston, Ontario) were performed with an Otter USV to compare the performance of various controllers over a predefined path, including an NMPC controller with an idealized model and a controller with a model generated using the SINDy algorithm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".