Development of a stochastic predictive PID controller
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Bibliographic record
Abstract
A new stochastic, predictive proportional-integral-derivative (PID) controller is proposed which is mathematically equivalent to generalized predictive control (GPC) with steady state weighting. The main motivation of this paper is the extension of the classical PID algorithm on industrial computers to implement advanced control without employing specialized software. Predictive PID controller constants and the internal model are chosen by equating the discrete PID control law to a linear form of GPC. Predictive PID is stochastic because GPC is based on a model of both the plant and the disturbance/noise term. Performance of the predictive PID scheme is shown, via simulation, to be identical to GPC. An industrial application of predictive PID using an existing control computer shows a significant improvement in performance compared to the existing PID scheme.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it