Stability and Performance Analysis of Centralized and Distributed Multi-rate Control Architectures for Multi-user Haptic Interaction
Bibliographic record
Abstract
This paper is concerned with multi-user haptic simulation environments in which users can interact across an Ethernet-based Local Area Network (LAN) or a Metropolitan Area Network (MAN). Using network protocols such as the UDP and TCP/IP under normal network traffic conditions, the achievable real-time packet communication rate can be well below the 1 kHz update rate suggested in the literature for high fidelity haptic rendering. However by adopting a multi-rate control strategy, the local control loops can be executed at a much higher rate than that of the data packet transmission between the user workstations. Within such a framework, two control architectures, namely centralized and distributed are presented. Mathematical models of the controllers are developed and used in a comparative analysis of their stability and performance. The results of such analysis demonstrate that the distributed control architecture has greater stability margins and outperforms the centralized controller. It is also shown that the limited network packet transmission rate can degrade the haptic fidelity by introducing a viscous damping into the perceived impedance of the virtual object. Using the proposed models, this damping value is calculated and compensated by active control. Experiments conducted with a dual-user/dual-finger haptic platform confirm the analytical results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".