Adaptive Energy Reference Time Domain Passivity Control of Haptic Interfaces
Bibliographic record
Abstract
Haptic devices are designed to assist humans in operating tasks in a remote or virtual environment. The passivity-based controllers feed back the forces from the environment while maintaining stability. This article presents the adaptive energy reference time domain passivity approach to overcome the sudden force change inherent in the conventional time domain passivity approach (TDPA). The main advantage of the proposed method is that it can be applied to the haptic interfaces interacting with delayed unknown environments without increasing conservatism compared to the conventional TDPA with or without energy reference. The adaptive energy reference is learned at each interaction by a passive estimation of the haptic interface energy. The energy reference is found using force and velocity data, which does not need the foreknowledge of the environment dynamic model parameters and time delay. Therefore, the designed controller can adapt to different environments and time delays. The proposed method is evaluated in both simulation and experimental setups where the parameters of the environments are unknown to the controller. It is shown that the sudden change in force is decreased compared to the conventional TDPA for haptic interface with or without time delay in the system.
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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".