Quick Suppression of Vibration of Robot via Hybrid Input Shaping Control Strategy
Bibliographic record
Abstract
When the vibration amplitude and resonant frequency bandwidth of each mode are different in the multiple-modal system, the response time of a system is increased but the residual vibration is effectively reduced by the positive impulses multiple-modal input shapers. However, a negative impulses hybrid multiple-modal input shaping method can solve those problems. The basic principle of this control strategy and a 3-DOF parallel robot were introduced. Six negative impulses hybrid input shapers to reduce vibration of the first two modes were constructed based on the robot. Using simulation methods, the response time and vibration suppression abilities of various negative impulses hybrid two-modal input shapers (NHTIS) were obtained by analyzing the vibration response curves of these input shapers, and comparing with positive and negative impulses two-modal input shapers, respectively. The results show that the NHTIS can improve the response speed of the system while significantly reducing the multiple-modal residual vibration.
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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) | 0.000 | 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".