Model-Free Control of a Class of High-Precision Scanning Motion Systems with Piezoceramic Actuators
Bibliographic record
Abstract
To enhance the precision of coarse long-stroke motion axes, complementary short-stroke fine positioning stages are usually introduced. Being mechanically attached, the motion of the combined positioning stages needs to be controlled and synchronized. Therefore, typically suitable model-based controllers of fine stages are designed according to the sophisticated models and identification techniques used. Due to their appealing features, Piezocermamic-based fine positioning stages were successfully utilized in many applications, which recently sparked their use in high-acceleration motion found in wafer scanners, for example, where high-precision motion is required despite the resulting high inertial forces involved. Unfortunately, hard nonlinear behavior is associated with piezoelectric actuators, which adds to the complexity of modeling, control, and synchronization processes. To overcome such a burden, in this study, the design procedure of a model-free control and synchronization technique of piezocermamic-based fine positioning stages is introduced and verified experimentally using a representative precision motion system comprising a planner stage and a uni-axial fine stage under step-and-scan trajectories commonly used in wafer scanners. Despite its simplicity, the herein proposed design procedure can be seamlessly extended to other robotics and automation applications.
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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 | 0.000 | 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".