Some Experimental Considerations of Stage Control in Digital Valves
Bibliographic record
Abstract
Because of saturation and hysteresis of magnetic materials, nonlinear characteristics are commonly experienced in servo or proportional valves. These nonlinearities can substantially affect the performance of the valve in practical applications. In the presence of magnetic nonlinearities, the output signal (displacement or force) is dependent on the input current and the sign of its derivative. If the driving current to the electrical-to-mechanical interface device changes for a number of cycles, as in a stepper motor for example, then a series of reset points will occur as the current undergoes cyclic changes. At each reset point the original starting characteristics of the system are re-established. A large number of reset points across the full stroke of the spool results in a significant reduction in the nonlinear behavior; indeed, the characteristics of the valve approach those of a linear system. The approach in creating these multiple reset points has been defined by the authors as “stage control”. In this paper, stage control using variable reluctance and hybrid stepper motors is first discussed. For the variable reluctance stepper motor, the reset point occurs once at each step of the stepper motor, whereas it occurs twice in a single cycle in the hybrid types. Experiments using a spool valve as a load were designed to obtain the characteristics using stage control. It is demonstrated that with the introduction of stage control, nonlinearities, such as saturation and hysteresis, are greatly reduced, system stiffness is increased, and the positioning accuracy and resolution of the spool are improved. The effect of dither due to a “digital fragment” signal is also examined and found to be crucial in reducing the hysteresis and in improving the resolution accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".