AN INDEPENDENT ACTIVE TORQUE BALANCER USING A SERVO-CONTROLLED DIFFERENTIAL GEAR TRAIN
Bibliographic record
Abstract
This paper proposes a novel concept of active balancer for reducing the input torque fluctuations of mechanisms. A differential gear train is used in this active balancer and one of its two input shafts is driven and controlled by a servomotor. From the structural point of view, it is designed as an independent device that can be assembled and disassembled easily; from the functional point of view, it can minimize the torque fluctuations in a variety of working conditions. At first, an exact control function of the servomotor that can totally eliminate the input torque fluctuations of the mechanism is gained by an analytical method; in what follows, an optimization approach is developed to select appropriate control functions for the servomotor to balance the input torque of the working mechanism with consideration of the servomotor’s own input torque minimization; finally, an integrated method is presented for optimizing both the control function of the servomotor and the structure parameters of the differential gear train. Two numerical examples are given to illustrate the design procedure and to show its feasibility.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".