A pragmatic approach towards assessment of control loop performance
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this paper, a pragmatic approach to process modelling and control loop performance assessment is proposed. We consider the following practical scenarios: the control loop of concern is operated by a simple controller such as PI/PID controller; a simple open‐loop/closed‐loop step response data is available or an open‐loop/closed‐loop step test can be readily performed; the control loop may be subject to significant disturbances and/or measurement noises. Our objectives are: (1) to estimate a continuous‐time process model from the step response data; (2) to assess control loop performance with a pragmatic benchmark in terms of both output performance and input variation, and identify practically attainable control loop performance. We will summarize the theory and algorithms developed for such a relatively comprehensive analysis. A number of simulation studies are presented to demonstrate the feasibility of the proposed methodology. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 |
| 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