Development and simulation of magnetorheological damper for segment erector vibration control
Bibliographic record
Abstract
In this paper, magnetorheological dampers are applied to a segment erector to replace passive vibration dampers. Because magnetorheological damper dynamics are highly nonlinear, design of a direct control system is impossible. To apply linear control theory directly to design the magnetorheological damper controller, the Takagi–Sugeno fuzzy model analytically represents the segment erector model. In addition, a disturbance observer based on a Takagi–Sugeno fuzzy controller is proposed for this system. Both simulations and experiments validate the performance enhancement and stability of the controller. The results show that the acceleration of the segment erector was reduced by 59.6% and 32.1% in oblique wave excitation and random excitation, respectively, compared to a conventional passive damper. The proposed fuzzy controller and magnetorheological dampers have great potential in practical applications because they can significantly improve the performance of a segment erector.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".