Empirical Comparison of Differential Evolution Variants for Industrial Controller Design
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Bibliographic record
Abstract
To be cost-effective, most commercial off-the-shelf industrial controllers have low system order and a predefined internal structure.When operating in an industrial environment, the system output is often specified by a reference model, and the control system must closely match the model's response.In this context, a valid controller design solution must satisfy the application specifications, fit the controller's configuration and meet a model matching criterion.This paper proposes a method of solving the design problem using bilinear matrix inequality formulation, and the use of Differential Evolution (DE) algorithms to solve the resulting optimization problem.The performance of the proposed method is demonstrated by comparing a set of ten DE variants.Extensive statistical analysis shows that the variants and are effective in terms of mean best objective function value, average number of function evaluations, and objective function value progression.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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