Development of Numerical Models Based on Experimental Tests for the Design of Active Vibration Controllers
Bibliographic record
Abstract
Abstract Active Vibration Control (AVC) of electromechanical systems has been an active field of research for decades. One main challenge in AVC techniques is the design of the control laws. In most cases, the design is entirely based on numerical models, thus making high-fidelity models greatly desirable. However, analytical or finite element models are intrinsically onerous and rarely consider imperfections of real-life structures. To overcome this drawback, this work presents a method to obtain a high-fidelity numerical model for electromechanical systems built upon the collection of a set of experimental data. From the experimental responses, the modal mass, damping, stiffness, and electromechanical coupling matrices are determined by using the least square method technique and error minimization. After building the model, the design of a Multiple Input Multiple Output (MIMO) Positive Position Feedback (PPF) control law is presented to mitigate vibrations on a composite panel equipped with 8 non co-located piezoelectric materials and free-free boundary conditions. The tuning of the MIMO controller is carried out completely on the numerical model. Once the tuning is completed, the resulting parameters of the controller are experimentally tested for validation. Experimental results show that the MIMO PPF efficiently reduces vibrations of several modes for both systems, with an overall reduction of nearly 50% compared to the uncontrolled case. In addition, the frequency bandwidth spans more than 150Hz with negligible spillover.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".