A trajectory tracking controller for an underwater hexapod vehicle
Bibliographic record
Abstract
This paper describes work done in the modeling and control of a low speed underwater vehicle that uses paddles instead of thrusters to move in the water. A review of previously modeled vehicles and of controller designs for underwater applications is presented. Then, a method to accurately predict the thrust produced by an oscillating flexible paddle is developed and validated. This is followed by the development of a method to determine the ideal paddle motion to produce a desired thrust. Several controllers are then developed and tested using a numerical simulation of the vehicle. We found that some model-based controllers could improve the performance of the system while others showed no benefit. Finally, we report results from experimental trials performed in an open water environment comparing the performance of the controllers. The experimental results showed that all the model-based controllers outperform the simple proportional-derivative controller. The controller giving the best performance was the model-based nonlinear controller. We also found that the vehicle was able to follow a change of a roll angle of 90 degrees in 0.7 s and to precisely follow a sinusoidal trajectory with a period of 6.28 s and an amplitude of 5 degrees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".