Dynamic analysis and controller design for a slider–crank mechanism with piezoelectric actuators
Bibliographic record
Abstract
Abstract Dynamic behaviour of a slider–crank mechanism associated with a smart flexible connecting rod is investigated. Effect of various mechanisms' parameters including crank length, flexibility of the connecting rod and the slider's mass on the dynamic behaviour is studied. Two control schemes are proposed for elastodynamic vibration suppression of the flexible connecting rod and also obtaining a constant angular velocity for the crank. The first scheme is based on feedback linearization approach and the second one is based on a sliding mode controller. The input signals are applied by an electric motor located at the crank ground joint, and two layers of piezoelectric film bonded to the top and bottom surfaces of the connecting rod. Both of the controllers successfully suppress the vibrations of the elastic linkage. Highlights Dynamic behaviour of a slider–crank mechanism associated with a smart flexible connecting rod is investigated. Effect of various mechanisms' parameters including crank length, flexibility of the connecting rod and the slider's mass on the dynamic behaviour is studied. Two control schemes are proposed for elastodynamic vibration suppression of the flexible connecting rod and also obtaining a constant angular velocity for the crank. Controllers are based on feedback linearization approach and sliding mode controller.
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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) | 0.002 | 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".