Model-based Decentralized Control of Time-delay Teleoperation Systems
Bibliographic record
Abstract
Recently, within a centralized control framework, the authors proposed a time-delay reduction method to achieve improved transparency in time-delay teleoperation. In this paper, a decentralized version of the controller is introduced that can further enhance the teleoperation transparency and robust stability by allowing the use of local delay-free measurements in local master/slave controllers. State/observation transformations are proposed that produce delay-free dynamics/measurements from the perspective of the local controllers at the master and slave stations. It is shown that the use of local suboptimal linear quadratic Gaussian master and slave controllers results in a delayed state perturbation term in the closed-loop dynamics. The stability of the system is then analyzed using a delay-dependent frequency sweeping test. The controllers employ a simple switching logic to handle large variations in the environment dynamics from free motion to rigid contact. The performance and robustness of the new decentralized controller are compared with those of the centralized controller using numerical analysis. Experimental results 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".