The Use of Damping Based Semi-Active Control Algorithms in the Mechanical Smart-Spring System
Bibliographic record
Abstract
In recent decades, semi-active control strategies have been investigated for vibration reduction. In general, these techniques provide enhanced control performance when compared to traditional passive techniques and lower energy consumption if compared to active control techniques. In semi-active concepts, vibration attenuation is achieved by modulating inertial, stiffness, or damping properties of a dynamic system. The smart spring is a mechanical device originally employed for the effective modulation of its stiffness through the use of semi-active control strategies. This device has been successfully tested to damp aeroelastic oscillations of fixed and rotary wings. In this paper, the modeling of the smart spring mechanism is presented and two semi-active control algorithms are employed to promote vibration reduction through enhanced damping effects. The first control technique is the smart-spring resetting (SSR), which resembles resetting control techniques developed for vibration reduction of civil structures as well as the piezoelectric synchronized switch damping on short (SSDS) technique. The second control algorithm is referred to as the smart-spring inversion (SSI), which presents some similarities with the synchronized switch damping (SSD) on inductor technique previously presented in the literature of electromechanically coupled systems. The effects of the SSR and SSI control algorithms on the free and forced responses of the smart-spring are investigated in time and frequency domains. An energy flow analysis is also presented in order to explain the enhanced damping behavior when the SSI control algorithm is employed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".