Smart Spring Impedance Control Algorithm for Helicopter Blade Harmonic Vibration Suppression
Bibliographic record
Abstract
In this paper, an adaptive impedance control algorithm is developed for the Smart Spring to suppress helicopter rotor vibration through individual blade control. The harmonic frequencies of the blade response were estimated using parametric methods, which were used to synthesize the reference signal. Multiple equivalent notches were formed at corresponding frequencies to selectively suppress the blade vibration modes. To improve the adaptability of the algorithm, an on-line control path identification method is presented. The algorithm was implemented on a MATLAB xPC platform using the hardware-in-the-loop concept. Closed-loop experiments, conducted using a fixed helicopter blade section in both shaker and wind tunnel tests, have achieved significant vibration suppressions. Experimental results demonstrate that the algorithm, which is able to select the blade vibration modes and track the variations in vibration due to changes in flight condition, is promising for helicopter individual blade control applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".