Model Predictive Control for Transparent Teleoperation Under Communication Time Delay
Bibliographic record
Abstract
Prior efforts in bilateral teleoperation under communication delay have mainly yielded control algorithms that sacrifice performance in order to guarantee robust stability. In contrast, this paper proposes a multimodel predictive controller that can enhance the teleoperation transparency in the presence of a known constant delay. Separate controllers are designed for free motion/soft contact and contact with rigid environments, with switching between these mode-based control laws occurring according to the identified contact mode. Performance objectives such as position tracking and tool impedance shaping for free motion/soft contact, as well as position and force tracking for contact with rigid environments, are incorporated into a multi-input/multi-output state-space representation of the system dynamics. New Artstein-type state and measurement transformations are proposed to generate delay-free dynamics suitable for output-feedback control, based on the original dynamics with delays in various input and output channels. The application of the continuous-time linear quadratic Gaussian control synthesis to the resulting mode-based delay-free dynamics yields control laws that guarantee closed-loop stability and enhanced performance in each phase of teleoperation. The robustness of the mode-based controllers with respect to parametric uncertainty is analyzed. Experimental results with a single-axis teleoperation setup demonstrate the effectiveness of the proposed approach
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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 | 0.001 |
| 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".