Bounded-Impedance Absolute Stability of Bilateral Teleoperation Control Systems
Bibliographic record
Abstract
Available passivity-based robust stability methods for bilateral teleoperation control systems are generally conservative, as they consider an unbounded range of dynamics for the class of passive operators and environments in the complex plane. In this paper, we introduce a powerful 3D geometrical robust stability analysis method based on the notions of wave variables and scattering parameters. The methodology, which was originally a 2D graphical method used in microwave systems for single-frequency analysis [1], is further developed in this paper for teleoperation and haptic systems. The proposed method provides both mathematical and visual aids to determine bounds or regions on the complex frequency response of the passive environment impedance parameters for which a potentially unstable system connected to any passive operator is stable, and vice-versa. Furthermore, the method allows for the design of bilateral controllers when such bounds are known, or can even be utilized when the environment dynamics are active. The geometrical test can also be replaced by an equivalent mathematical condition, which can easily be checked via a new stability parameter. The proposed method results in less conservative guaranteed stability conditions compared to the Llewellyn's criterion; thus, promising a better compromise between stability and performance. The new method is numerically evaluated for two bilateral control architectures.
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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 | 0.003 |
| 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 | 0.001 |
| 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".