Harmonic active vibration control using piezoelectric self-sensing actuation with complete digital compensation
Bibliographic record
Abstract
In this article, the implementation of a self-sensing piezoelectric actuator with complete digital compensation is presented. The proposed compensation not only minimizes the signal due to the electrical behavior of the self-sensing actuator but also takes into account the fact that piezoelectric actuator causes a local strain—not related to the global vibration of the plate—in a vibrating plate to which it is coupled. Therefore, the corrected measured current is related to the global vibration of the plate and may be used in an active control scheme. The electro-mechanical model on which is based this self-sensing actuator is first explained. Then, the electronic and digital processing implementation is presented, as well as the active time-harmonic control scheme used. Finally, results of experimental validation are presented, and the attenuation performance of the self-sensing actuator is compared to the performances of a co-localized accelerometer/lead zirconate titanate pair. It is shown that the corrected self-sensing actuator current gives results better than what is obtained with a co-localized sensor/actuator pair and that this technique may be used to control more than one frequency simultaneously.
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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 | 0.001 | 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".