Structural Design of Supreme Controller with Uncontrollable Transitions
Bibliographic record
Abstract
This paper proposes an iterative control policy to design a less restrictive and admissible Petri net (PN) controller for a Discrete Event System (DES) with uncontrollable transitions, when a maximally permissive controller is not obtains by.This paper exploits a structural supervisory control method, avoiding reachability graph.This exciting method addresses the controllability condition of the desired functioning PN to define it as General Mutual Exclusion constraints (GMEC), which leads to design place invariant-based controller.The controller is non-admissible if one control place is connected to uncontrollable transition.To obtain an admissible PN controller, if such a controller exists, authors propose constraints transformation, which is computationally complex, while the controller arcs displacement approach is unsystematic.Based on it, we develop the idea to iterate the structural supervisory control method, to ensure that control place is connected to controllable transition.Through this, it was found that the displacement of arcs is systematic and the controller is less restrictive and admissible, namely the supreme controller.The results indicate that the linear constraints are never violated through the firing of uncontrollable transitions.The finding of this work may serve to evaluate the structural optimality of the controller, in order to perform it practically.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".