EXPERIMENTAL IDENTIFICATION OF ELECTROMECHANICAL COUPLING MATRICES FOR ACTIVE VIBRATION CONTROL
Bibliographic record
Abstract
PPF (Positive Position Feedback) is a highly effective algorithm for controlling mechanical vibrations in thin-walled structures.It is easy to use with piezoelectric patches and has useful modal characteristics.However, when using a MIMO (Multi-Input Multi-Output) architecture, it is necessary to convert physical signals from piezoelectric transducers into modal coordinates, which is done by placing participation matrices between sensors, controllers, and actuators.These matrices can be difficult to determine, especially when the number of actuators does not match the number of modes being controlled.Typically, an electromechanical FE (Finite Element) model or reduced-order model is used to estimate the participation matrices.This study proposes a method for estimating participation matrices using only experimental measurements.The method is tested on a two-dimensional composite plate with free edges, which has eight vibration modes.The plate's vibrations are controlled using four sensors and four actuators in a non-collocated configuration.The experimental identification of the electromechanical coupling allows for the simulation of uncontrolled and controlled vibrations of the plate when it is subjected to external disturbance.The resulting PPF AVC (Active Vibration Control) significantly reduces the vibration amplitude of all eight modes in laboratory experiments, with minimal impact on the following normal modes.The vibration reduction is verified at various points on the plate's surface when it is subjected to pseudo-random excitation.The proposed experimental identification technique greatly simplifies the design of PPF controls and makes AVC techniques more widely accessible by eliminating the need for electromechanical modeling.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".