Modeling, Identification, and Adaptive Robust Motion Control of Voice-Coil Motor Driven Stages
Bibliographic record
Abstract
Voice-coil motors are widely used in precision motion control of industrial applications such as head positioning of hard disk drives, semiconductor fabrication and packaging. In this paper, to achieve high precision movement potential of vertical voice-coil motor driven stages, the accurate modeling of nonlinear rigid body dynamics is developed, and the model parameters are estimated by the identification experiments in time domain. The neglected high-frequency dynamics are also identified through frequency response experiments to verify the validity of the frequency range of the proposed rigid-body dynamical model. To attenuate the serious nonlinear effect of the plant dynamics, Coulomb friction compensation is used when obtaining the frequency response results. Based on the verified nonlinear rigid-body dynamical model, an adaptive robust controller is developed to obtain a guaranteed performance in the presence of both parametric uncertainties and uncertain nonlinearities. Comparative control experimental results obtained show the effectiveness and good tracking performance of the proposed ARC algorithm.
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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) | 0.000 | 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 | 0.001 | 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".