Learning-Based Guidance and Control Codesign for Underactuated Autonomous Surface Vehicles: Theory and Experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional sideslip angle estimator compensation methods used in line-of-sight (LOS) guidance law are not effective when facing with big amplitude and fluctuating sideslip angle scenarios, resulting in poor path following performance of underactuated autonomous surface vehicles (ASVs). To overcome the drawback, a novel sideslip angle estimator based on long short-term memory (LSTM) is proposed in this article. It integrates the selective updated strategy (SUS) to enhance the learning capability and long-term memory of extremely fluctuating temporal information, thereby meeting the requirement of estimating fluctuating sideslip angle. Based on the proposed SUS-LSTM sideslip angle estimator, a learning line-of-sight (LLOS) guidance law for path following is designed. Furthermore, we theoretically prove the input-to-state stability in probability of the closed-loop cascaded control system, which consists of LLOS and heading controller. Finally, the proposed algorithm is implemented on ASVs and experiments are conducted in the Lingshui bay to validate the superiority and effectiveness of the algorithm.
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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 | 0.001 | 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 | 0.001 |
| 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