Suppressing operator-induced oscillations in manual control systems with movable bases
Bibliographic record
Abstract
There are many manual control tasks in which the operator's action is fed back to the input device, usually a joystick, through the operator's body dynamics excited by the base motion. This can lead to instability and reduced performance. This paper proposes a novel approach to the cancellation of such "biodynamic feedthrough". A prototype single-degree-of-freedom task in which the operator uses a force-reflecting joystick to position his/her base is considered here. A model-based approach is used to formulate /spl mu/-synthesis-based controllers that coordinate the motions of the joystick and the base. The solution is obtained by D-K iterations. The resultant controllers are robustly stable with respect to variations in the arm/joystick and biodynamic feedthrough parameters. They also provide a desired level of performance based upon position tracking between the joystick and the base and admittance shaping of the joystick. Experimental studies demonstrate the effectiveness of the proposed methods in the suppression of feedthrough induced oscillations. The approach developed in this paper, with some modifications, can be generalized to teleoperation from movable bases.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".