USING ARTICULATED BODY ALGORITHM WITHIN SLIDING-MODE CONTROL TO COMPENSATE DYNAMIC COUPLING IN UNDERWATER-MANIPULATOR SYSTEMS
Bibliographic record
Abstract
A control scheme is presented for compensating dynamic coupling between an underwater robotic vehicle (URV) and a manipulator. During task execution the torques commanded at the manipulator joints lead to reactions at the junction point of the manipulator and vehicle. These reactions disturb the vehicle position and orientation and are the source of the vehicle-manipulator coupling. In many underwater robotic vehicle-manipulator (URVM) applications, the URV serves as a base while the manipulator performs a required task. Therefore, it is necessary to hold the URV as stationary as possible. In the current work, Slotine’s sliding mode control approach is used to compensate the dynamic effect of the underwater manipulator on the URV. The articulated body (AB) algorithm is used both for the time-domain simulation of the system and for the dynamic equations within the model-based sliding-mode controller. The AB algorithm is preferred for the time-domain system simulation, as it provides a computationally efficient simulation scheme. Finally, a three DOF manipulator mounted on a URV is considered, and results of time-domain numerical simulations of the proposed control scheme are presented.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".