Hybrid Analog/Digital Control of Bilateral Teleoperation Systems
Bibliographic record
Abstract
Hybrid analog/digital control of bilateral teleoperation systems can lead to superior performance (transparency) while maintaining stability compared to pure analog or digital control methods. Such hybrid control is preferable over pure analog control, which is inflexible and not ideal for realizing complex teleoperation control algorithms, and pure digital control, which restricts teleoperation performance due to a well-known stability-imposed upper bound on the product of the digital controller's proportional gain and the sampling period. In this paper, a hybrid controller combining a Field programmable analog array (FPAA) based analog controller and a personal computer-based digital controller is compared in terms of performance and stability to its analog and digital counterparts. A stability analysis indicates that the addition of analog derivative term widens the range of teleoperation controls gains that satisfy the stability conditions, paving the way for improving the teleoperation performance. We also show how the hybrid controller leads to better teleoperation performance. To this end, we study the human's performance of a switch flipping task and a stiffness discrimination task in the teleoperation mode. In both tasks, the hybrid analog/digital controller allows the human operators to achieve the highest task success rates.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".