High-Precision Heading Control of an Autonomous Sailboat: A Robust Nonlinear Approach
Bibliographic record
Abstract
This article investigates high-precision robust heading control for an autonomous sailboat. First, the mathematical model of an autonomous sailboat is highlighted and discussed, with a particular focus on the heading dynamics that must be robustly controlled under real-world conditions. Typical control difficulties such as system disturbances, modeling uncertainty, control actuation saturation, and measurement noise are considered and addressed with reference to the controller and system modeling. We propose a control method that ensures robust heading control of an autonomous sailboat in the presence of these challenges. A reference simulator in Matlab/Simulink is used as a simulation testbed to demonstrate the benefits of state-of-the-art robust control developments. A multiphase simulation is conducted to compare the advantages and disadvantages of model-based robust control with linear techniques such as proportional-integral-derivative (PID) control.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".