Substructuring Dynamic Modeling and Active Vibration Control of a Smart Parallel Platform
Bibliographic record
Abstract
A substructuring approach to derive dynamic models for closed-loop mechanisms is applied to model a flexible-link planar parallel platform with Lead Zirconate Titanate (PZT) transducers. The Lagrangian Finite Element (FE) formulation is used to model flexible linkages, in which translational and rotary degrees of freedom exist. Craig-Bampton mode sets are extracted from these FE models and then used to assemble the dynamic model of the planar parallel platform through the application of Lagrange’s equation and the Lagrange multiplier method. Electromechanical coupling models of surface-bonded PZT transducers with the host flexible linkages are introduced to the reduced order dynamic models of flexible linkages. The assembled system dynamic model with moderate model order can represent essential system dynamic behavior and maintain kinematic relationships of the planar parallel platform. A Proportional, Integral, and Derivative (PID) control law is used as the motion control law. Strain rate feedback (SRF) active vibration control is selected as the vibration control law. Motion control simulation results with active vibration control and simulation results without active vibration control are compared. The comparison shows the effectiveness of active vibration control.
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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 | 0.001 | 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".