Modal Control Design of Configuration-Dependent Linkage Vibration in a Parallel Robot Through Experimental Identification
Bibliographic record
Abstract
Modal control algorithms have been widely used in suppressing structural vibration, where vibration characteristics are linear and constant. This paper presents experimental work demonstrating the application of modal control to closed-loop mechanisms, where flexible deformation is coupled with nonlinear rigid body motion. A PRR experimental planar parallel robot is used as the test platform. This lightweight planar parallel manipulator is designed to improve operational speed of "pick-and-place" processes and implement a "smart parallel manipulator" through the integration of a parallel mechanism architecture and active control of linkage vibration using lead zirconate titanate (PZT) transducers. Boundary conditions and mode shapes of intermediate linkage are not conventional due to the fact that the linkages undergo constrained rigid body motion. Experimental modal analysis (EMA) is used to determine the boundary conditions of flexible linkages. However, it is observed that linkage vibration exhibits configuration-dependency. Based on experimental observations, an assumption is taken to simplify the transfer function from the motor input to linkage vibration. Based on this simplification, a modal controller is designed and implemented. Experimental results demonstrate dramatic linkage vibration reduction
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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) | 0.000 | 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) | 0.002 | 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".