Active vibration suppression of smart laminated beams using layerwise theory and an optimal control strategy
Bibliographic record
Abstract
Vibration suppression of laminated composite beams using the smart structures concept is presented in the present work. The smart system consists of a laminated composite beam as the host structure and piezoceramic and PVDF patches as the actuation and sensing elements. To treat the material and geometric inhomogeneities through the thickness of the laminated smart structure, a finite element model based on the layerwise displacement theory which incorporates the electro-mechanical coupling effects has been developed. The state space model of the active laminated beam is then used to design the control system. A linear quadratic regulator (LQR) controller is designed to achieve vibration suppression of the laminated smart beam. The effects of the laminate configuration and locations of sensors/actuators on controlled response are investigated. An experimental set-up has been developed to determine the natural frequency and damping factor of the smart laminated beam. The experimental measurements are then used to design a control mechanism with LQR to suppress the vibration response of the system. Open-loop and closed-loop responses of the system have been obtained experimentally and compared with the corresponding simulation results to demonstrate the accuracy and efficiency of the present approach in the vibration control of laminated smart structures.
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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".