A Time Domain Passivity Approach for Asymmetric Multilateral Teleoperation System
Bibliographic record
Abstract
Time-domain passivity control (TDPC) is a widely used reliable approach to ensure the stability of the teleoperation systems. Although TDPC almost guarantees the stability of the system, there can be a possibility of instability as TDPC inherits the zero division problem in its conventional control laws. Low value force or velocity signals can lead to zero division in control laws and can be a reason of control failure. A multilateral teleoperation system is much more complex as compared to a bilateral teleoperation system. A multilateral teleoperation system comprises of multiple humans and multiple master and slave robots which results in increased transmission of signals over a communication network. So, it is substantially invaluable for multilateral teleoperation system to have a control scheme which ensures a safe and stable operation. This paper, an extension of our previous work, presents a novel design of TDPC laws for multilateral teleoperation, which not only maintains the passivity of the system for stability but also avoids zero division, thus guaranteeing a stable operation. A new architecture of communication channel is introduced to assign different weights to the masters and slaves depending upon the task requirements. The control strategy proposed in this paper is valid for different numbers of masters and slaves. Simulation and experimental results are presented to demonstrate the efficacy of the control design.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".