Active vibration control of a curved sandwich beam using a nonlinear PPF algorithm
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Bibliographic record
Abstract
Nonlinear behaviour resulting from large-amplitude vibrations is often categorized as softening or hardening, according to the direction of the shift in the natural frequency. While nonlinear hardening systems have been controlled in the past, often using positive position feedback (PPF) and both linear and nonlinear algorithms, softening systems have often been overlooked. In this experimental study, a curved sandwich beam with clamped boundary conditions when subject to large external forcing was subjected to various active vibration controllers based on linear and nonlinear PPF algorithms. The curved beam is a softening system presenting both quadratic and cubic stiffness terms. Piezoelectric patches were used as controller transducers. Both linear and nonlinear controller types were tested, with nonlinear controllers including both quadratic and cubic gain terms. Furthermore, single-input single-output (SISO) and multi-input multi-output (MIMO) architectures were both tested, with SISO controllers including only a single actuator and sensor pair and MIMO controllers involving two actuators and two sensors. While increasing the amplitude of the nonlinear terms in the not-optimized controller was found to significantly improve vibration attenuation, the optimized controller showed a smaller improvement following the introduction of nonlinear terms in its algorithm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it