H-inf Design and mu-Analysis Based Optimal Mapping of Sensors and Actuators in Flexible Structures
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a computationally efficient technique for the determination of the optimal size and spatial mapping of distributed actuators on a flexible structure to suppress vibrations in a H∞ control design framework. The cost of the computations required in the H∞ based optimization algorithm is reduced by using an efficient feasibility test. The feasibility test penalizes the candidates for the actuator size and locations resulting in the open-loop zeros remaining closer to the imaginary axes and passes the ones moving the open-loop zeros farther left of the imaginary axis. Then, by using only the candidates passing this feasibility test, optimization of the actuator size and placement can be performed using the H∞ based design and μ analysis. The optimal mapping technique presented in this study is demonstrated on a simple finite element based model of a flexible structure consisting of a cantilevered beam with two pairs of spatially non-collocated distributed actuators and a displacement sensor.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it