Hierarchical interface-based supervisory control of a flexible manufacturing system
Bibliographic record
Abstract
Flexible manufacturing systems have long been touted as an application area for supervisory control theory. Unfortunately, due to the typical exponential growth of state space with the number of interacting subsystems, concurrent systems such as manufacturing applications have, for the most part, remained beyond the reach of existing supervisory control theory tools. This paper demonstrates how, by imposing a hierarchical, modular, interface-based architecture on the system, significant gains can be made in the size of applications that can be handled by supervisory control theory. We first review hierarchical interface-based supervisory control, providing the theory necessary to motivate the creation of well-defined automata-based interfaces between components. This architecture permits the verification of global safety (controllability) and nonblocking properties to be decomposed into a set of local checks, each of which only involves an individual component subsystem and its interface automata. The paper then provides a detailed description of how the theory can be applied to the design and verification of a flexible manufacturing system work cell. The work cell model is based on the Atelier Intere/spl acute/tablissement de Productique flexible manufacturing workcell, a system that has been previously studied in the literature with limited success.
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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 | 0.000 |
| 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".