DESIGN OF A FUZZY-PID CONTROLLER FOR A NANOSCALE X-Y PLATFORM
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Bibliographic record
Abstract
A fuzzy-PID controller design was proposed in this paper for a nanoscale platform positioning system. The target nanoscale X-Y platform is mounted on a pantograph mechanism and restricts the rotation by two small sliders; the device is driven by a traditional X-Y platform with common precision. The goal of this study is to drive the target platform’s movement in the region of 5 mm × 5 mm for the positioning, with repeated positioning accuracy error less than 200 nm by the traditional X-Y platform. Due to the different PID parameters affecting platform positioning accuracy and system response for the two axes, the fuzzy-PID controller will train to fit the mechanism to promote the positioning precision and path control effect. The simulation and experimental results indicated that the proposed method is feasible for nano-scale micro-platform positioning.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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