Dynamics and control of tethered underwater-manipulator systems
Bibliographic record
Abstract
In this work, the dynamics modelling strategy of a tethered underwater remotely operated vehicle (ROV) coupled with a and spatial manipulator have been studied. With regards to the cable dynamic modelling, it is considered to be a series of lumped point masses connected by linear, massless, visco-elastic springs. In addition, the model accounts for the tether bending and twisting effects. Regarding the manipulator dynamics, the Articulated-Body Algorithm is employed due to its computational efficiency. In order to control the ROV motion under disturbance forces and moments caused by the tether and the manipulator motion, a series of Model-based SISO sliding-mode controllers are implemented and the ABA is used to predict the dynamic coupling force expressions based on the feedback of ROV and the manipulator states. The control gains of the sliding-mode controllers are defined as a closed function of the articulated inertias of the ABA algorithm; leading to time varying gains as opposed to constant as in conventional methods. As a case study, a Saab-Seaeye FALCON™ ROV with a modified Hydrolek™ HLK 43000 manipulator is presented. Numerical simulations are performed to reveal the extent to which the tether dominates the Falcon-manipulator dynamics. It is shown that disturbance forces and moments created by tether motion must be actively compensated using while the ROV is held stationary during manipulator operation. It is also shown that the use of force sensors at the FALCON™'s tether termination can dramatically improve the performance of the series of SISO sliding mode controllers.
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 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".