Active Vibration Control of Piezoelectric Composite Plates Using Gain Scheduling Method
Bibliographic record
Abstract
This paper presents an active vibration control methodology to suppress the vibration of a square laminated composite plate.The plate undergoes large deflection, where the dynamics of the system is nonlinear.The main layer of the composite plate is made of a graphite/epoxy composite (T300/976) with angle orientations of [-45/45/-45/45], with a total thickness of 1mm, in addition to two piezoelectric layers of type PZT G1195 N, each with a thickness of 0.1 mm.The two piezoelectric layers cover the entire area of the upper and lower surfaces of the laminated composite plate where the upper layer senses vibration and feeds it back to the controller, whereas the lower layer acts as an actuator which receives a voltage signal from the controller.The proposed control method involved using a Proportional-Integral-Derivative (PID) controller with a gain scheduling strategy where the system nonlinearity is addressed by changing the controller parameters momentarily, with optimal tuned parameters selected according to the dynamic characteristics during the vibration of the plate.The numerical model of the laminated composite plate is formulated using the finite element method (FEM) for large deflection vibrating composite plates.The controller performance was tested by simulating the vibration of the plate under the action of the controller for four cases with different vibration modes, and the results showed the effectiveness of the proposed PID controller with gain scheduling strategy.
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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".