Disturbance-Observer-Based Sliding-Mode Control for a 3-DOF Nanopositioning Stage
Why this work is in the frame
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Bibliographic record
Abstract
To compensate for the nonlinear effects of nanopositioning stages and their model uncertainties, several control methods have been developed and reported in the literature. One promising method for compensation is the use of a proportional-integral-derivative (PID)-based sliding-mode control (SMC), in which the nonlinear effects are treated as an unknown disturbance to the system. If the nonlinearity and the model uncertainties can be completely or partially estimated, integration of their estimations into the control schemes may lead to improved performance. On this basis, this paper presents the development of a disturbance-observer-based (DOB) SMC, in which the nonlinearity of the nanopositioning stage is partially predicted through the use of an observer and then compensated by the PID-based SMC. Experiments were performed to verify the effectiveness of the proposed control schemes, and the results showed that the performance of the nanopositioning stage by employing the DOB-SMC was greatly improved, as compared to the PID-based SMC.
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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 | 0.000 |
| 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) | 0.000 | 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