Adaptive Energy Reference Time Domain Passivity Control of Teleoperation Systems in the Presence of Time Delay
Bibliographic record
Abstract
The main goal of the teleoperation systems is to achieve the highest transparency possible while maintaining stability in the presence of the time delay. This paper proposes the adaptive energy reference Time Domain Passivity Approach (TDPA) to teleoperation systems with communication delays, in order to overcome the drawbacks of the conventional TDPA such as sudden force change, conservatism, and position drift. The proposed method establishes a reference energy function by estimating the passive elements of the system, i.e., eliminating the active parts. The passive elements are estimated by utilizing the Recursive Least Square (RLS) method. The controller makes the system follow the reference energy by dissipating energy with a variable damping element. Since the controller is activated once the energy decreases, it has smoother force changes than the conventional TDPA which is activated once energy gets negative. The adaptive energy reference TDPA is extended for teleoperation systems in a structure that can avoid the position drift and the conservative passivity condition of the conventional TDPA by eliminating the parallel passivity controller. The simulation results show that the adaptive energy reference TDPA reduces the force changes up to 23 percent in comparison with the conventional TDPA. In addition, the energy of the adaptive energy reference TDPA is less dissipated than the conventional TDPA which shows a less conservative behavior. Furthermore, no position drift is observed in the adaptive energy reference TDPA.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".