Performance Assessment of Industrial Linear Controllers in Univariate Control Loops for Both Set Point Tracking and Load Disturbance Rejection
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Bibliographic record
Abstract
This paper studies the performance assessment of linear controllers in univariate feedback control loops, where the processes to be controlled can be approximated by first-order plus dead time models, and the set points are subject to ramp or step changes. The lower bound of the total variation (TV) of control signals is established. Taking the lower bound of TV and that of integrated absolute error (IAE) as benchmarks, an IAE-TV-based performance index is proposed. Four industrial linear control schemes are investigated to find out the conditions simultaneously achieving satisfactory performances in terms of input load disturbance rejection and set point tracking subject to a constraint on the robustness. A novel performance assessment method is presented to calculate the proposed performance index from measurements. Numerical and experimental examples are provided to validate the performance benchmark and the effectiveness of the proposed performance assessment method.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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