A Comparison of Intelligent PID Position Controllers With Autotuners for a Pneumatic System
Bibliographic record
Abstract
This paper reports on a study whose objective is to explore the potential of intelligent algorithms such as fuzzy rules and neural networks (NN) as applied to the control of pneumatic servosystems. Current application is position control of a pneumatic gantry robot. Preliminary experiments with adaptive fuzzy and NN controllers showed improvement in tracking performance by upwards of 70%. This level of improvement was expected given the adaptive nature of both controllers. However, both methods also required significant effort to setup. An on-line autotuner was developed to improve the ease of implementation. Comparative results are given for five controllers: 1) manually tuned PID, 2) autotuned PID, 3) adaptive NN PID, 4) fuzzy adaptive PID and 5) autotuned fuzzy adaptive PID. Experiments were conducted at 2 different supply pressures and 3 different tracking frequencies. Once again the fuzzy adaptive PID controller improved performance over fixed gain PID, this time by upwards of 80%, even when both were given the benefit of autotuning.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".