شناسایی سیستم و طراحی کنترل بهینه با استفاده از الگوریتم ژنتیک برای کنترل ارتعاشات یک بال هوشمند
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A solution to the problem of identification and control of smart structures is presented in this paper. Smart structures with build-in sensors and actuators can actively and adaptively change their physical geometry and properties. As a particular example, a representative dynamic model of a typical fighter vertical tail, identified as the smart fin, is considered. Piezoelectric patches, which are mounted on the vertical tail, are employed as actuator in the model. The Frequency Response Function (FRF) of the smart fin is obtained from experiment. The corresponding transfer function is then derived using classic system identification (ID) techniques, using MATLAB® system identification toolbox, which is verified with the experimental data. The model obtained using system ID is then used to tune an optimal PID controller to reduce the vibration of the smart structure. To this end, several cost functions are defined and optimized by a genetic algorithm. Next, the obtained controllers are compared with each other and a suitable one is chosen as the system’s controller. Finally, It is shown in simulations that the designed controller is able to reduce the vibration of the smart fin very well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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