Generative Programming for Programmable Logic Controllers
Bibliographic record
Abstract
Many attempts have been made to implement supervisors derived by synthesis procedures peculiar to the supervisory control theory (SCT), most adopting the event-based supervisory control paradigm. However, when considering code generation schemata for programmable logic controllers (PLCs), hardware resources are limited and event tracking is hard to realize satisfactorily. Moreover, previous work has highlighted differences between the abstract model adopted by SCT and realistic process control situations. Inappropriate solutions to these issues may result in code generation schemata that produce unreliable PLC code. A generative programming approach for PLCs based on a dual paradigm, the state-based supervisory control paradigm, is investigated in this paper. Such an approach exhibits interesting properties. For instance, the maximum depth of the PLC stack as well as PLC cycle timing evaluations become possible. Furthermore, well-known code optimization techniques can be used to obtain more efficient code.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".