Experimental Studies on an Adaptive Tuned Mass Damper with Real-Time Tuning Capability
Bibliographic record
Abstract
In this paper, experimental and simulation results on the control of structural vibrations using a newly proposed adaptive pendulum mass damper with real-time tuning capability are presented. The adaptive pendulum mass damper is a spherical pendulum augmented with a tuning frame to adjust its length and two adjustable air dampers connected to the mass for achieving the damping adjustment. The mechanical adjustments are implemented using three independent stepper motors, one micro-controller and three drives. The mass damper is used to control the vibration responses of a bench-scale two-story model structure with sufficiently long fundamental period representative of flexible structures such as towers. The basic architecture of the system proposed consists of two components; identification and control, one followed by the other in that order. The identification is carried out using traditional Fourier methods and a second-order blind identification method in the time-domain. The control phase consists of position control based on the identified frequency from the identification phase. The paper focuses on the hardware and software aspects of the real-time implementation of this adaptive mass damper. The identification methods used in this study rely only on acceleration measurements collected from high accuracy-low frequency accelerometers mounted on the structure, and do not utilize the excitation information. This study is primarily intended to demonstrate the feasibility of employing adaptive pendulum mass damper designs to enhance the robustness of passive tuned mass dampers to structural, environmental and design changes.
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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 | 0.002 |
| 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 | 0.001 |
| 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".