Process control performance evaluation in the case of variable set‐point with experimental applications
Bibliographic record
Abstract
Abstract The purpose of this paper is to contribute with some refinements to recent methods of analysis of control loop performance, based on the well‐established principle of Internal Model Control (IMC). Lower limits for the absolute value of the integral of control error (IAE) and the total variation of control action (TV) are assumed as reference values for a control considered good or at least acceptable. The overall performance index assumes as a benchmark a controller tuned according to rules of S(implified)IMC technique and is appropriately defined with respect to the lower limits of the two metrics IAE and TV. This allows the assessment of control loop performance, that is, the validity of tuning for PID‐type controllers in response to different types of reference change. In fact, one can assess performance in the case of set‐point changes as steps, ramps, or generic varying trends over time. In order to demonstrate the validity of the refined technique, several examples of simulation, case studies on a pilot plant, and real industrial data are presented.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".