Parameter identification of Bouc–Wen dynamic model for magnetorheological shimmy damper based on improved simulated annealing algorithm
Bibliographic record
Abstract
Magnetorheological (MR) shimmy damper has a good application prospect in aircraft landing gear shimmy control as a semi‐active vibration control device; however, its non‐linear and hysteretic characteristics bring difficulties to the control and restrict performance. It is necessary to develop a dynamic model of the damper that can effectively show these characteristics. This study is based on the experimental data of the damping force characteristics of MR shimmy damper with different control currents. Bouc–Wen model, apply to describe non‐linear and hysteretic characteristics, was selected to establish the dynamic model of damping force, displacement and velocity. This study proposed an improved simulated annealing (SA) algorithm, which can improve the efficiency of identification, to identify the parameters of the model. Comparing with the original algorithm, the improved SA algorithm has the same solution quality and better performance in computational efficiency. The relationships between the identified parameters and the control current were obtained by curve fitting, and the experimental data with different amplitudes and frequencies are used to verify the result. It is shown that the established model can accurately show the dynamic characteristics of the damper under different excitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".