Active Vibration Control of a Composite Sandwich Plate
Bibliographic record
Abstract
The active vibration control of a rectangular sandwich plate by Positive Position Feedback is experimentally investigated. The thin walled structure, consisting of carbon-epoxy outer skins and a Nomex paper honeycomb core, has completely free boundary conditions. A detailed linear and nonlinear characterization of the vibrations of the plate was previously performed by our research group [1, 2]. Four couples of unidirectional Macro Fiber Composite (MFC) piezoelectric patches are used as strain sensors and actuators. The positioning of the patches is led by a finite element modal analysis, in the perspective of a modal control strategy aimed at the lowest four natural frequencies of the structure. Numerical and experimental verifications estimate the resulting influence of the control hardware on the modal characteristics of the plate. Experimental values are also extracted for the control authority of the piezoelectric patches in the chosen configuration. Single Input – Single Output (SISO) and MultiSISO Positive Position Feedback algorithms are tested and the transfer function parameters of the controller are tuned according to the previously known values of modal damping. A totally experimental procedure to determine the participation matrices, necessary for the Multiple-Input and Multiple-Output configuration, is developed. The resulting algorithm proves successful in selectively reducing the vibration amplitude of the first four vibration modes in the case of a broadband disturbance. PPF is therefore used profitably on laminated composite plates in conjunction with strain transducers, for the control of the low frequency range up to 100 Hz. The relevant tuning procedure moreover, proves straightforward, despite the relatively high number of transducers. The rigid body motions which arise in case of free boundary conditions do not affect the operation of the active 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 | 0.000 | 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".