Improved Seven-Segment Acceleration/Deceleration Algorithms Based on Cosine and Exponential Functions for Vibration Suppression of Multi-Axis Robotic Arms
Bibliographic record
Abstract
As core equipment in intelligent manufacturing, residual vibration of robotic arms during high-speed motion directly affects positioning accuracy and service life. Focusing on vibration suppression in multi-axis robotic arms, this paper addresses the limitations of the traditional seven-segment S-curve (7S) acceleration/deceleration algorithm in jerk continuity and vibration control. Two improved seven-segment algorithms based on cosine and exponential functions are proposed to enhance vibration suppression performance by smoothing jerk transitions. Mathematical models of the traditional 7S, seven-segment cosine (7S-Cos), and seven-segment exponential (7S-Exp) algorithms are established. Experiments are conducted under practical operating conditions with a 4 kg load at full speed, using joint synchronization control to evaluate end-effector residual vibration. Results show that the 7S-Cos algorithm achieves the smallest vibration amplitude and fastest attenuation, significantly outperforming the traditional 7S and 7S-Exp algorithms while maintaining motion efficiency. The proposed method provides a practical solution for vibration-sensitive robotic applications such as precision assembly and high-speed handling.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".