Proportional-integral-derivative λ-tuning for integrating processes with deadtime
Bibliographic record
Abstract
Over the past 15 years, a number of model-based proportional-integral-derivative (PID) tuning methods have been developed for systems that can be described as integrating-with-deadtime. This was motivated by the observation that the initial open-loop response of lag-dominant processes, such as tray temperature in high-purity distillation columns, resembles that of a delayed ramp variable. This study compares the behaviour of several PID λ-tuning rules with the Skogestad internal model control (or SIMC) proportional-integral (PI) controller on simulated integrator-plus-deadtime and lag-dominant first-order plants. Guidelines are provided for selecting the most appropriate tuning method for a given application based on the primary function of the feedback loop (servo against regulatory) as well as the relative importance of control effort and robustness. In contrast, the PID version of the Tyreus–Luyben controller, a popular strategy for this class of models, was found to yield excellent setpoint following but sluggish rejection of unmeasured disturbances acting at the plant input.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".