Decentralized harmonic active vibration control of a flexible plate using piezoelectric actuator-sensor pairs
Bibliographic record
Abstract
We have investigated decentralized active control of periodic panel vibration using multiple pairs combining PZT actuators and PVDF sensors distributed on the panel. By contrast with centralized MIMO controllers used to actively control the vibrations or the sound radiation of extended structures, decentralized control using independent local control loops only requires identification of the diagonal terms in the plant matrix. However, it is difficult to a priori predict the global stability of such decentralized control. In this study, the general situation of noncollocated actuator-sensor pairs was considered. Frequency domain gradient and Newton-Raphson adaptation of decentralized control were analyzed, both in terms of performance and stability conditions. The stability conditions are especially derived in terms of the adaptation coefficient and a control effort weighting coefficient. Simulations and experimental results are presented in the case of a simply supported panel with four PZT-PVDF pairs distributed on it. Decentralized vibration control is shown to be highly dependent on the frequency, but can be as effective as a fully centralized control even when the plant matrix is not diagonal-dominant or is not strictly positive real (not dissipative).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".