Experimental comparison of two pneumatic servo position control algorithms
Bibliographic record
Abstract
Many researchers have investigated pneumatic servo positioning systems due to their numerous advantages: inexpensive, clean, safe and high ratio of power to weight. However, the compressibility of the working medium, air, and the inherent non-linearity of the system continue to make achieving accurate position control a challenging problem. In this paper two control algorithms are designed for the pneumatic servo problem and their experimental performance is compared. The first algorithm uses position plus velocity plus acceleration feedback combined with feedfoward and deadzone compensation (PVA+FF+DZC). The second algorithm is a form of sliding-mode control (SMC). Extensive experiments using different payloads (1.9, 5.8 and 10.8 kg), vertical and horizontal movements, and move sizes from 3 to 250 mm were conducted. Averaged over 70 experiments with various operating conditions, the tracking error for SMC was 59% less than with PVA+FF+DZC. For a 5.8 kg payload and a 0.5 Hz, 70 mm amplitude, sine wave reference trajectory the root mean square error with SMC was less than 0.4 mm for both vertical and horizontal motions. This tracking control performance is better than those previously reported for similar systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".