Optimized Active Control of a Smart Cantilever Beam Using Genetic Algorithm
Bibliographic record
Abstract
Vibration is one of the most dangerous phenomena that happens to a structure. It leads to premature fatigue and eventually failure, with potentially fatal consequences. A smart structure is an excellent solution to this problem; it adds an actuator, a sensor, and an appropriate control law to the system to reduce/eliminate the vibration. This study developed a complete analytical model for a cantilever beam with a collocated PZT sensor/actuator pair. First, we used a coupling of a collocated PZT sensor and an actuator to measure and control vibration levels based on a PID control law considering the physical constraints associated with PZT operation as the voltage level of the actuator. Next, the damping coefficient of the structure was determined by using genetic algorithms best fit to satisfy specific vibration conditions. Finally, we conducted a complete optimization for sensor/actuator position and PID parameters, using genetic algorithms. Thus, this paper gives a thorough understanding of the potential vibration control of the cantilever beam.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".