Discrete-time Linear Quadratic Gaussian Control for Teleoperation Under Communication Time Delay
Bibliographic record
Abstract
Prior relevant research in bilateral teleoperation has mainly yielded control algorithms that sacrifice performance in order to guarantee robust stability in the presence of communication latency. In contrast, in this paper we propose a multimodel predictive-type control approach based on the discrete-time linear quadratic Gaussian (LQG) control that delivers a stable transparent response in the presence of constant delay. Separate controllers are designed for different phases of operation, i.e., free motion/soft contact and contact with rigid environments, with switching between these mode-based controllers occurring according to the identified contact mode. The treatment of the problem in the discrete-time domain allows for the development of a finite dimension state-space model that explicitly encompasses the time delay. 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 the LQG control design framework. The robustness of the controller with respect to uncertainty in the system parameters is examined via the Nyquist analysis. Simulation and experimental results demonstrate that the proposed control technique is highly effective in providing a stable transparent interface for teleoperation under time delay.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".