Decoupled modelling and controller design for the hybrid autonomous underwater vehicle
Bibliographic record
Abstract
The autonomous underwater vehicle (AUV) MACO was developed at the University of Victoria, in partnership with Defence Research and Development Canada (DRDC) as part of a feasibility study. DRDC was interested in investigating the use of an AUV to support rapid deployment of acoustic element arrays. The requirements on the AUV to stop and hover, while triggering a low frequency sound source, lead to the multiple thruster, hybrid design of MACO. This thesis presents the development of MACO with the primary focus on the AUV dynamics modelling and its controller design. The project commenced with the development of the vehicle’s mechanical and software systems, followed by the collection of the open-loop experimental data. This data was used to produce drag and inertial parameters, which were used during the dynamics modeling process for each degree of freedom (surge, yaw, heave, and pitch). Next, discrete controllers based on PID, feed forward, and velocity feedback were added to each model along with discretely represented sensors in the feedback loop. The closed loop responses of each simulated
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".