Design of a robust internal model control PID controller based on linear quadratic gaussian tuning strategy
Bibliographic record
Abstract
A design procedure of the robust IMC‐PID controller based on the linear quadratic Gaussian (LQG) tuning strategy is proposed to avoid the cut‐and‐try method in tuning the internal model control proportional integral derivative (IMC‐PID) controller parameters. In this paper, the relationship between the optimal controller and the maximum sensitivity function is established. Then the relationship between the maximum sensitivity function and the IMC‐PID parameter is established. The IMC‐PID parameter can be tuned by the above two relationships. In other words, the tuned IMC‐PID parameter depends on the optimal controller parameter which is designed by the LQG tuning strategy. The application to design a process of the IMC‐PID controller shows the effectiveness of the proposed approach for the first order plus the dead time (FOPDT) system and the second order plus the dead time (SOPDT) system. Simulation results present that the proposed method shows the tradeoff between the dynamic performance and the system robustness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".