Truss Structures with Piezoelectric Actuators and Sensors
Bibliographic record
Abstract
Abstract There are two possible ways of dealing with flexibility in a structure: use stiffer structural members, which increase weight, or augment the inherent damping in the structure. Due to weight restrictions, only the second approach is feasible. In this article, the analytic and experimental development for the active vibration suppression of a flexible truss structure with piezoelectric actuators and sensors is explained. The structure is said to be smart, as it incorporates sensors, actuators and the intelligence to react to disturbances. Finite element modelling and modal decomposition techniques are used to obtain an accurate model of the complex dynamics. The relations that govern the response of the piezoelectric sensors and the influence of the piezoelectric actuators are derived. These relations are presented in such a way that they are easily integrated into the finite element modelling framework. Piezoelectric actuators and sensors are used as components of an active control system for the truss structure that is designed using robustH∞optimal control theory. Stimulations are performed on both the open and closed loop dynamics to assess the amount of additional damping obtained by the active control system. Simulation results show that the controller significantly increases the damping of the structure. The active controller has been implemented on an experimental truss structure. The experiments performed on the structure closely matched the simulation results.
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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) | 0.001 | 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".