DIGITAL REDESIGN OF A STEPPING-MOTOR DRIVER IN THE PRESENCE OF COMPUTATIONAL DELAYS AND DISTURBANCES
Bibliographic record
Abstract
A method of digital redesign that can take computational delays and disturbances into account is presented and applied to an industrial analog driver for stepping motors. The method is based on the so-called Plant-Input-Mapping (PIM) method, which guarantees the stability for any non-pathological sampling interval and is extended to the case where computational delays are present. The delay, which does not have to be integral multiple of the sampling period, is considered to be a part of the plant so that adverse effects of the delay on the digital controller performance can be taken into account. Since the effects of disturbances in the motor should also be dealt with in the driver, modifications to the PIM are incorporated such that the characteristics from the disturbance to the plant-input can be adjusted without affecting those from the reference input. The performance of the resulting PIM method is investigated experimentally and found to be very close to that of the analog original, which cannot be recreated using the commonly used Tustin’s method at a sampling rate suitable for commercial production.
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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) | 0.002 | 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".