Characterization, modeling and vibration control of a flexible joint for a robotic system
Bibliographic record
Abstract
This paper presents the experimental characterization and vibration control of a flexible robotic system. For this work, a test bench was built to characterize the harmonic drive (HD) and joint components, while control algorithms were designed and compared to minimize vibration. Encoder accuracy was critical since the difference in the measurements between two encoders was used to evaluate the vibrational behavior of the test set-up. Therefore, a laser tracker was used to characterize the error of the output encoder. Real-time compensation using this technique achieved an angular position accuracy of 50 µrad. Four rosette strain gauges were fixed to the HD’s flexible spline to determine its torsion. To reduce torque ripple, a real-time correcting function was applied. It was thus possible to reduce the error to 0.3% of the full-scale error. Two vibration control strategies were developed, namely, singular perturbation and feed-forward control. Simulation results showed that both control strategies greatly reduced vibration response compared to a common rigid control. However, test results showed that good vibration control could only be achieved with the feed-forward approach: the singular perturbation technique generated too much torque ripple to the motor. A feed-forward controller can quickly stabilize the link, achieving the same settling time as with the rigid control algorithm.
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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".