ADAPTIVE ROBUST TRACKING CONTROL OF AN UNDERWATER VEHICLE-MANIPULATOR SYSTEM WITH SUB-REGION AND SELF-MOTION CRITERIA
Bibliographic record
Abstract
This paper proposes an adaptive robust control scheme for an Underwater-Vehicle Manipulator System (UVMS). The proposed controller enables the tracking of the intersection of multiple local sub-regions that are assigned for each subsystem of a UVMS under the influence of modelling uncertainties as well as additive disturbances. The presence of variable ocean currents creates hydrodynamic forces and moments that are not well-known or predictable, even though they are bounded. Therefore, the control task of tracking a prescribed sub-region trajectory is challenging due to these additive bounded disturbances. In the presented adaptive control law, a least-squares estimation algorithm is utilized rather than gradient-type approach. The use of the self-motion due to the kinematically redundant system allows performance of multiple subtasks (e.g., maintaining manipulability and avoidance of mechanical joint limits). The asymptotically sub-region and sub-task tracking are ensured using the proposed control law despite the parametric uncertainty of the UVMS and external additive disturbances. The stability analysis is carried out using the Lyapunov-type approach. The simulation results illustrate the validity of the proposed control scheme.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".