Intelligent built-in torque sensor for harmonic drive systems
Bibliographic record
Abstract
A harmonic drive is a compact, light-weight and high-ratio torque transmission device which is used in many electrically actuated robot manipulators. In this paper a built-in torque sensor for harmonic drive systems is examined in detail. The method proposed by Hashimoto (1989), in which strain-gauges are directly mounted on the flexspline, is employed and improved. To minimize sensing inaccuracy, four Rosette strain gauges are used employing an accurate positioning method. To cancel the torque ripples, the oscillation observed on the measured torque and caused mainly by gear teeth meshing, Kalman filter estimation is used. A simple forth order harmonic oscillator proved to accurately model the torque ripples. Moreover, the error model is extended to incorporate any misalignment torque. By on line implementation of the Kalman filter, it has been shown that this method is a fast and accurate way to filter torque ripples and misalignment torque. Hence, the intelligent built-in torque sensor is a viable and economical way to measure the harmonic drive transmitted torque and to employ that for torque feedback strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".