An experimental approach to multi-input multi-output nonlinear active vibration control of a clamped sandwich beam
Bibliographic record
Abstract
Large amplitude vibrations are often associated with geometric nonlinearity. These nonlinear systems are usually controlled using linear controllers, such as positive position feedback (PPF). Nonlinear control has also often been limited to single-input single-output (SISO) architectures. The present study develops a nonlinear PPF controller implemented with both a SISO and a multiple-input multiple-output (MIMO) architecture on a real experimental set-up, consisting of a clamped composite sandwich beam. The controller aimed to reduce the vibration amplitude observed when the structure was subject to a stepped-sine excitation in the frequency neighborhood of the first natural frequency for different force excitation levels. The developed nonlinear controller is based on a linear PPF controller to which a cubic term was added since the system under study is of hardening type. The control parameters were selected by optimization of the reduction of the vibration at low excitation amplitude. The nonlinear MIMO and SISO versions of the controller were compared to linear MIMO and SISO versions, which omitted the cubic term but maintained the other parameters. The nonlinear controllers were found to outperform the linear ones, and the MIMO versions of each controller also outperformed the SISO ones. Overall, the experimental evidence shows that a nonlinear MIMO PPF controller should be preferred for the application under study.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".