Experimental Implementation on Vibration Mode Control of a Moving 3-PRR Flexible Parallel Manipulator with Multiple PZT Transducers
Bibliographic record
Abstract
This paper presents the experimental implementation of active vibration control applied to a moving 3-PRR parallel manipulator with three flexible links. The active vibration control is implemented using three piezoelectric (PZT) transducer pairs applied to one flexible intermediate link based on modal strain rate feedback (MSRF) control. A real-time active vibration control system is developed using two PCs with LabVIEW Real-Time. Modal analyses are conducted, and the results demonstrate that the vibration modes of the intermediate links are dynamically coupled and the vibration frequency components are complicated and closely spaced. Simplified and efficient modal filters are developed to extract modal coordinates in real time, and a second order compensator is used to filter amplified noises and unmodeled high frequency dynamics. A MSRF controller is then designed using an independent mode space control strategy, and is implemented experimentally with the first mode targeted for control. Experimental results demonstrate that the vibration amplitude of the first vibration mode of the intermediate link is significantly reduced using three PZT control pairs. The experimental results using one PZT control pair and two control pairs are provided to justify the use of three PZT control pairs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".