Dynamic Surface Control of Cooperating Hydraulic Manipulators in the Presence of Friction
Bibliographic record
Abstract
This paper presents development of a new controller that allows two or more hydraulically-actuated robots to cooperatively move a rigid object along a desired trajectory while sharing the load and maintaining an acceptable internal force on the object. The effects of friction (represented by LuGre model in this paper), as well as parametric uncertainties in the manipulators' dynamics, hydraulic functions and the payload, are all accommodated through augmentation of the controller by a set of on-line updating laws. In arriving at this new controller, the concept of dynamic surface control is adopted to prevent the "explosion of terms" that was observed in our previously developed controller [10]. Therefore, the new controller has the benefit of not requiring an acceleration observer or the derivative of terms that could be difficult to numerically achieve. Stability analysis shows that the controller guarantees arbitrarily small bounded tracking error even in the presence of nonlinear model uncertainties. Both simulation and experimental results are shown to illustrate the effectiveness of the developed controller for tracking tasks.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".