A novel semi-active switching control scheme for magnetorheological elastomer-based vibration isolator under dynamic input saturation
Bibliographic record
Abstract
Abstract It is still a challenge to guarantee a robust performance of magnetorheological elastomer (MRE)-based isolators under complex and frequency-varying base excitation, due to the highly nonlinear dynamic behavior of the MRE. The dynamic behavior of the MRE-based isolator should be considered properly in the control law to ensure the effectiveness of the MRE-based isolator. In this research study, first a dynamic modeling of an MRE-based isolator is established to formulate the actuation (transmitted) force of the MRE-based isolator. The MRE induced actuation force is limited to a dynamic input saturation and depends on the applied magnetic field density, geometry of the MRE layers, viscoelastic properties of the MREs and system states. Subsequently, a novel semi-active switching control scheme, which adopts the fastest convergence speed of driving the virtual energy toward zero based on Lyapunov theory, is proposed to improve the mitigation performance of the MRE-based isolator. The proposed control law requires less modeling information and does not require a time-consuming adjustment of control parameters, thereby possessing a broad application prospect in MRE-based isolators. Finally, the performance of the MRE-based isolator using the developed control strategy is compared with those based on passive control with different levels of constant magnetic field density, ON–OFF control and PD control under the harmonic, random and bump shock base excitation. The results show a robust and superior performance of the developed vibration control strategy.
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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".