Optimal Design of Multiple Tuned Mass Dampers to Reduce Vibrations of a Ram-Type Structure With Varying Dynamics via a Control Theoretic Framework
Bibliographic record
Abstract
Abstract This paper investigates the use of a linear time-invariant (LTI) control framework to optimally design multiple tuned mass dampers (TMDs) that minimize unwanted vibrations caused by exogenous disturbance forces to a ram-type structure with varying dynamic characteristics. A key challenge for the development of the LTI control framework is the reformulation of the TMDs’ design parameters, which consist of linear and nonlinear parameters as static feedback gains. This paper proposes the use of extra cascade control inputs to reformulate the optimization problem into an LTI control framework for the simultaneous optimization of linear (i.e., stiffness and damping) and nonlinear (i.e., location) parameters. A rigid planar system with multiple attached TMDs is developed as a mathematical model. It is reconstituted as an LTI framework by connecting a control input for the location parameter with control inputs for the stiffness and damping parameters. The model is then optimized using multi-model H∞ synthesis. A commercial gantry-type machining center is used to validate the proposed approach. Results from the simulation and experiment show that the optimized multiple TMDs systematically designed by this approach improve the system's dynamic stiffness by up to 83% and increase the allowable maximum depth of cut from 1 mm to 1.5 mm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".