A FEM model for active vibration control of flexible linkages
Bibliographic record
Abstract
For the design of active vibration control laws for flexible linkages in high-speed mechanisms, dynamic models incorporating the coupling of rigid body motion and flexible motion and the electromechanical coupling of transduction devices and the host linkage are very important. In the first part of this paper, the Lagrange finite element (FE) formulation is used to derive such a dynamic model for a flexible planar linkage with one rotary and two translational degrees of freedom. Linear electromechanical coupling of surface-bonded lead zirconate titanate (PZT) patches with the host linkage is incorporated into the model. In the second part of this paper, this dynamic model is simplified and applied to simulate active vibration control of flexible linkages in a high-speed planar parallel platform based on kineto-elastodynamics assumptions: (1) the influence of flexible motion on rigid body motion is ignored; and (2) joint constraint forces in the flexible linkage case are close to those in the rigid linkage case. Based on these assumptions, the rigid body motion and joint constraint forces are regarded as inputs to flexible dynamics, which is obtained from rigid body simulation. Using the simplified dynamic model, simulation of strain rate feedback control using PZT sensors and actuators is performed. Experimental results are also presented. Both simulation and experimental results show that PZT actuators effectively damp the vibration of the flexible linkages.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".