Design of performance-adaptive PID controllers
Bibliographic record
Abstract
In the challenge to manufacture high quality products for less, it is necessary to regularly monitor performance of control loops that regulate the quality variables of interest. This paper describes performance-adaptive PID control schemes which is based on a unified approach to the design of a control performance and PID controller. According to the proposed approach, the control performance is first monitored regularly. Then, if the performance exceeds a user-defined threshold, the system identification is initiated and PID parameters are subsequently updated for the new model. In this paper, two performance-adaptive PID controller design schemes are introduced. One is that the control performance is evaluated based on the minimum variance index of the control error, and PID parameters are retuned based on the relationship of the generalized predictive control. Another is that the modeling performance is first evaluated, and PID parameters are subsequently calculated based on the LQG trade-off curve obtained for the re-identified process model. The behavior of the performance-adaptive PID control schemes is numerically and experimentally evaluated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".