Design of performance-adaptive PID controllers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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