Robust controller design for teleoperation systems
Bibliographic record
Abstract
The controller design for a bilateral teleoperation system involves trade-offs between performance and robust stability. Beyond simple intuition, little is known how performance and robust stability trade off. This paper shows that it is possible to achieve robust stability and nominal performance of a bilateral teleoperation system by using a four-channel control architecture. The controller design problem is formulated as a multiple objective optimization problem, which is shown to be convex if parametrizing all stabilizing controllers via the Youla parametrization. Performance specifications, such as kinematic correspondence error, force tracking error, etc., are defined; and robust stability is also incorporated into the controller design. The controller design problem is formulated as a multiple objective optimization problem, which is shown to be convex if parametrizing all stabilizing controllers via the Youla parametrization. The limit of performance achievable with the designed controller, thus the exact form of the trade-offs between performance and robust stability can be computed numerically. To demonstrate those, this paper treats the design of a controller for a simple one degree-of-freedom (DOF) system model of a motion-scaling teleoperation 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".